A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | |
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1 | Cambricon | Hardware | Accelerator | B | 200 | 2016 | China | http://www.cambricon.com/ | Cambricon Technologies builds core processor chips for intelligent cloud servers, intelligent terminals, and intelligent robots. | ||||||||||||||||
2 | Cerebras | Hardware | Accelerator | C | 112 | 2016 | Bay Area | https://www.cerebras.net/ | AI insights, faster Cerebras is a computer systems company dedicated to accelerating deep learning. The pioneering Wafer-Scale Engine (WSE) – the largest chip ever built – is at the heart of our deep learning system, the Cerebras CS-1. | ||||||||||||||||
3 | Graphcore | Hardware | Accelerator | E | 682 | 2016 | UK | Raised 372M | https://www.graphcore.ai/ | Graphcore has built a new type of processor for machine intelligence to accelerate machine learning and AI applications for a world of intelligent machines. | |||||||||||||||
4 | Groq | Hardware | Accelerator | 62.3 | 2016 | Bay Area | Raised unknown | https://groq.com/ | The Next Generation of Computing is here. | ||||||||||||||||
5 | Lightelligence | Hardware | Accelerator | A | 36 | 2017 | Boston | Raised 26M | https://www.lightelligence.ai/ | Accelerate AI, Neuromorphic, AI Chip, Optical Computing, Lightmatter | |||||||||||||||
6 | Luminous Computing | Hardware | Accelerator | A | 9 | 2018 | Bay Area | Raised unknown | https://luminous.co/ | Hardware is bottlenecked by data movement & compute. We use photonics to solve both | |||||||||||||||
7 | Nuvia | Hardware | Accelerator | B | 293 | 2019 | Bay Area | Raised 240M | https://nuviainc.com/ | Silicon design reimagined for a compute-intensive world. | |||||||||||||||
8 | SambaNova | Hardware | Accelerator | C | 465.3 | 2017 | Bay Area | Raised 250M | https://sambanova.ai/ | SambaNova Systems is a computing startup focused on building machine learning and big data analytics platforms. | |||||||||||||||
9 | Wave Computing | Hardware | Accelerator | E | 203.3 | 2008 | Bay Area | https://wavecomp.ai/ | Wave Computing is revolutionizing AI and deep learning with its dataflow-based systems and embedded solutions. | ||||||||||||||||
10 | Alectio | Modeling & Training | Active learning | 0 | 2019 | Bay Area | https://alectio.com/ | Not all data is created equal You can build better models with less data. We can show you how. | |||||||||||||||||
11 | CleverHans | Modeling & Training | Adversarial robustness | 2017 | Bay Area | OSS | http://www.cleverhans.io/ | An adversarial example library for constructing attacks, building defenses, and benchmarking both | |||||||||||||||||
12 | Cubonacci | All-in-one | AI Apps platform | 0 | 2018 | Netherlands | https://www.cubonacci.com/ | Machine learning lifecycle management Cubonacci enables organizations to focus on developing custom machine learning models without having to worry about peripheral matters. The Cubonacci platform manages deployment, versioning, infrastructure, monitoring and lineage for you, eliminating risk and minimizing time-to-market. | |||||||||||||||||
13 | Dataiku | All-in-one | AI Apps platform | D | 246.8 | 2013 | NYC | Raised 100M | https://www.dataiku.com/ | Dataiku's single, collaborative platform powers both self-service analytics and the operationalization of machine learning models in production. | |||||||||||||||
14 | DataRobot | All-in-one | AI Apps platform | F | 750.6 | 2012 | Boston | Raised 320M | https://www.datarobot.com/ | DataRobot combines a trusted enterprise AI platform and a trusted AI-native strategic partnership for global enterprises that want to harness the power of AI and their existing teams to succeed in today's Intelligence Revolution. | |||||||||||||||
15 | H2O | All-in-one | AI Apps platform | D | 151.1 | 2012 | Bay Area | OSS | https://www.h2o.ai/ | H2O.ai is the creator of H2O the leading open source machine learning and artificial intelligence platform trusted by data scientists across 14K enterprises | |||||||||||||||
16 | Iguazio | All-in-one | AI Apps platform | C | 72 | 2014 | Israel | https://www.iguazio.com/ | The Iguazio Data Science Platform automates your machine learning pipeline, transforming AI projects into real-world business outcomes. | ||||||||||||||||
17 | kedro | All-in-one | AI Apps platform | McKinsey | 2019 | UK | OSS | Kedro is an open source development workflow tool that helps structure reproducible, scaleable, deployable, robust and versioned data pipelines. | |||||||||||||||||
18 | Obliviously AI | All-in-one | AI Apps platform | Seed | 0 | 2018 | Bay Area | Raised unknown | https://www.obviously.ai/ | The entire process of running Data Science - building Machine Learning algorithm, explaining results and predicting outcomes, packed in one single click. | |||||||||||||||
19 | Peltarion | All-in-one | AI Apps platform | A | 36.8 | 2005 | Sweden | https://peltarion.com/ | A single AI platform, for real world deployments, without code. Fast & Efficient Production of AI Applications. Rich data capability. Develop AI Services fast. Usable & Affordable AI. | ||||||||||||||||
20 | Snorkel AI | All-in-one | AI Apps platform | A | 15.3 | 2019 | Bay Area | OSS | Raised 15M | https://snorkel.ai | Programmatically Building and Managing Training Data | ||||||||||||||
21 | Stradigi AI | All-in-one | AI apps platform | A | 40 | 2017 | Canada | Raised 40M | https://www.stradigi.ai/ | Stradigi AI's powerful AI business platform, Kepler, fuels tangible results for enterprises. No AI or machine learning experience required. | |||||||||||||||
22 | Xpanse AI | All-in-one | AI Apps platform | 2015 | Ireland | https://xpanse.ai/ | The power of AI at the click of a button. Xpanse AI brings easy to use and lightning fast analytics to your business. | ||||||||||||||||||
23 | Sisu | Data pipeline | Analytics platform | B | 66.7 | 2018 | Bay Area | https://sisudata.com/ | Sisu is the fastest, most comprehensive augmented analytics platform letting you ... You can't keep up with changing metrics using manual data exploration. | ||||||||||||||||
24 | Streamlit | Modeling & Training | App interface | A | 27 | 2018 | Bay Area | OSS | Raised 21M | https://www.streamlit.io/ | Streamlit is an open-source app framework for Machine Learning and Data Science teams. Create beautiful data apps in hours, not weeks. All in pure Python. | ||||||||||||||
25 | Dash | Serving | App interface | Plotly | 2015 | Canada | https://plotly.com/dash/ | Dash Enterprise is the end-to-end development & deployment platform for low-code AI Dash applications. | |||||||||||||||||
26 | Gradio | Serving | App interface | 0 | 2018 | Bay Area | OSS | 1.0 released | https://gradio.app/ | Gradio allows you to quickly create customizable UI components around your TensorFlow or PyTorch models, or even arbitrary Python functions. Mix and match | |||||||||||||||
27 | Plotly | Serving | App interface | B | 19.8 | 2013 | Canada | Raised 8.1M | https://plotly.com | Plotly is a data science and AI company that makes it easy to create and deploy interactive web apps in any programming language. | |||||||||||||||
28 | Abacus AI | All-in-one | AutoML | B | 53.3 | 2019 | Bay Area | Raised 48M | https://abacus.ai/ | Abacus.AI makes it effortless to create large-scale customizable deep learning systems. Accurate predictions generated by our system can be easily and securely incorporated into all aspects of your customer experience and business processes | |||||||||||||||
29 | Determined AI | Modeling & Training | AutoML | A | 13.6 | 2016 | Bay Area | https://determined.ai/ | Our AutoML platform streamlines your deep learning workflows, tracks your work, and manages your GPU clusters. | ||||||||||||||||
30 | Tazi.ai | Modeling & Training | AutoML | Seed | 1.22 | 2015 | Turkey | https://www.tazi.ai/ | TAZI’s Automated Machine Learning is understandable continuous machine learning from data and humans, enables business domain experts to use machine learning to make predictions and take actions. It also helps data analysts and scientists for their daily model creation and deployment. | ||||||||||||||||
31 | TPOT | Modeling & Training | AutoML | UPen | 2016 | Pennsylvania | OSS | A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. | |||||||||||||||||
32 | TransmogrifAI | Modeling & Training | AutoML | Salesforce | 2017 | Bay Area | OSS | an AutoML library for building modular, reusable, strongly typed machine learning workflows on Apache Spark with minimal hand-tuning | |||||||||||||||||
33 | DAWNBench | Modeling & Training | Benchmarking | Stanford | 2018 | Bay Area | OSS | DAWNBench is a benchmark suite for end-to-end deep learning training and inference. | |||||||||||||||||
34 | MLPerf | Modeling & Training | Benchmarking | 2018 | Bay Area | OSS | Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services. | ||||||||||||||||||
35 | Enflame | Hardware | Chips | 278.5 | 2018 | China | https://www.enflame-tech.com/ | Enflame’s AI development focuses on deep learning approaches used in cloud data centers, and are aimed at cloud AI training applications. | |||||||||||||||||
36 | Argo | Serving | CI/CD | Intuit | 2018 | Bay Area | OSS | https://argoproj.github.io/ | Get stuff done with Kubernetes. Open source Kubernetes native workflows, events, CI and CD | ||||||||||||||||
37 | RelicX | Serving | CI/CD | 0 | 2020 | Bay Area | New | https://relicx.ai | RelicX is a venture funded startup building an AI DevOps platform that brings CX intelligence into the CI/CD pipeline to ensure software release readiness based on real user behavior and customer experience. | ||||||||||||||||
38 | Anyscale | Infrastructure | Cloud management | B | 60.6 | 2019 | Bay Area | Raised 40M | https://www.anyscale.com/ | From the creators of Ray, a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab. | |||||||||||||||
39 | Cloudera | Infrastructure | Cloud management | IPO | 1000 | 2008 | Bay Area | https://www.cloudera.com/ | Cloudera delivers an Enterprise Data Cloud for any data, anywhere, from the Edge to AI. | ||||||||||||||||
40 | Datadog | Infrastructure | Cloud management | IPO | 147.9 | 2010 | NYC | https://www.datadoghq.com/ | See inside any stack, any app, at any scale, anywhere. | ||||||||||||||||
41 | Domino Data Lab | Infrastructure | Cloud management | E | 123.6 | 2013 | Bay Area | Raised 43M | https://www.dominodatalab.com/ | Deliver winning models. One place for your data science tools, apps, results, models, and knowledge | |||||||||||||||
42 | FloydHub | Infrastructure | Cloud management | Seed | 4 | 2016 | Bay Area | https://www.floydhub.com/ | FloydHub is a zero setup Deep Learning platform for productive data science teams. | ||||||||||||||||
43 | HYCU | Infrastructure | Cloud management | 0 | 2009 | Boston | https://www.hycu.com/ | Keep hyper-converged infrastructure running with HYCU's powerful, simple backup & recovery and monitoring solutions. Deploy in seconds for superior results. | |||||||||||||||||
44 | Paperspace | Infrastructure | Cloud management | A | 23 | 2014 | NYC | https://www.paperspace.com/ | GPU cloud tools built for developers. Powering next-generation workflows and the future of intelligent applications. | ||||||||||||||||
45 | MMdnn | Serving | Compatibility | Microsoft | 2017 | Seattle | OSS | MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML. | |||||||||||||||||
46 | ONNX | Serving | Compatibility | 2018 | OSS | https://onnx.ai/ | ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers. | ||||||||||||||||||
47 | Picsell.ia | All-in-one | Computer Vision | 0 | 2020 | France | Raised 18.6M, new | https://picsellia.com/ | Picsell.ia is a development platform dedicated to Computer Vision. From open-source to business, you can create and review datasets, track your experiments and follow your project in a Lean AI mode. | ||||||||||||||||
48 | Supervisely | All-in-one | Computer vision | 0 | 2017 | Bay Area | https://supervise.ly/ | First available ecosystem to cover all aspects of training data development. Manage, annotate, validate and experiment with your data without coding. | |||||||||||||||||
49 | Matroid | Modeling & Training | Computer vision | B | 33.5 | 2016 | Bay Area | Raised 20M | https://www.matroid.com/ | Computer vision made simple. Deploy computer vision solutions in minutes, not months. | |||||||||||||||
50 | DefinedCrowd | Data pipeline | Data generation | B | 63.6 | 2015 | Seattle | Raised 50.5M | https://www.definedcrowd.com/ | Leverage machine learning technology and human intelligence to source, structure, and enrich high quality training data in speech, NLP, and computer vision. | |||||||||||||||
51 | Kimono Labs | Data pipeline | Data generation | Palantir | 5 | 2014 | Bay Area | http://www.kimonolabs.com/ | Kimono Labs is an online platform that allows its users to convert their websites into APIs. | ACQ | |||||||||||||||
52 | Scale AI | Data pipeline | Data generation | D | 277.6 | 2016 | Bay Area | Raised 155M | https://scale.com | Trusted by world class companies, Scale delivers high quality training data for AI applications such as self-driving cars, mapping, AR/VR, robotics, and more. | |||||||||||||||
53 | Scrapinghub | Data pipeline | Data generation | 0 | 2010 | Ireland | OSS | https://scrapinghub.com/ | Turn websites into data with the world's leading web scraping services & tools from the creators of Scrapy. Data extraction trusted by industry leaders. | ELI5 | |||||||||||||||
54 | Synthetaic | Data pipeline | Data generation | 4.5 | 2019 | Wisconsin | Raised 4.5M, new | http://www.synthetaic.com/ | We grow high-quality data that unlocks impossible AI. What if edge cases no longer existed? What if training data was no longer a constraint? | ||||||||||||||||
55 | Databricks | All-in-one | Data management | F | 897 | 2013 | Bay Area | https://databricks.com/ | Unified Data Analytics Platform - One cloud platform for massive scale data engineering and collaborative data science. | ||||||||||||||||
56 | Petuum | All-in-one | Data management | B | 108 | 2016 | Pennsylvania | https://petuum.com/ | Petuum accelerates and simplifies AI solutions so your enterprise can deploy it easily and maintain it effortlessly. | ||||||||||||||||
57 | Alluxio | Data pipeline | Data management | B | 23 | 2015 | Bay Area | OSS | Raised 7M | https://www.alluxio.io/ | an open source data orchestration layer that brings data close to compute for big data and AI/ML workloads in the cloud. | ||||||||||||||
58 | Alteryx | Data pipeline | Data management | IPO | 163 | 2011 | LA | https://www.alteryx.com/ | We are a leader in the self-service data analytics movement with a platform that can discover, prep, and analyze all your data, then deploy and share analytics at scale for deeper insights faster than you ever thought possible. | ||||||||||||||||
59 | Aparavi | Data pipeline | Data management | 0 | 2016 | LA | https://www.aparavi.com/ | Aparavi's highly scalable data intelligence and automation solutions enable organizations to easily discover, classify, protect, and optimize their data. | |||||||||||||||||
60 | Ascend.io | Data pipeline | Data management | A | 19 | 2015 | Bay Area | https://www.ascend.io/ | Experience continuously optimized data pipelines with less code and fewer breakages. Enter the new era of data engineering with Ascend's autonomous dataflow service. | ||||||||||||||||
61 | AtScale | Data pipeline | Data management | D | 95 | 2013 | Bay Area | https://www.atscale.com/ | Freedom of choice for the enterprise. Break free the complexities and security risks associated with cloud migration and self-service analytics with Intelligent Data Virtualization—no matter where dat. | ||||||||||||||||
62 | Cazena | Data pipeline | Data management | 38 | 2014 | Boston | Raised unknown | https://www.cazena.com/ | First Data Lake with a SaaS Experience. Cazena empowers enterprises to collect, store and analyze any data in the cloud, without any DevOps resources or admin time. Cazena's Data Lake as a Service includes everything, and is delivered as secure SaaS, ready to load, store and analyze data with any method: SQL, Spark, R, Python, and many more. | ||||||||||||||||
63 | Cohesity | Data pipeline | Data management | E | 410 | 2013 | Bay Area | Raised 250M | https://www.cohesity.com/ | Eliminate mass data fragmentation with Cohesity's modern approach to data management, beginning with backup. Gain instant recovery. Learn more today. | |||||||||||||||
64 | Dremio | Data pipeline | Data management | C | 126.5 | 2015 | Bay Area | Raised 81.5M | https://www.dremio.com/ | Get more value from your data, faster. Dremio makes your data engineers more productive, and your data consumers more self-sufficient. | founders of the Apache Arrow and Apache Drill | ||||||||||||||
65 | erwin | Data pipeline | Data management | Parallax Capital Partners | 2016 | NYC | https://erwin.com/ | Integrated enterprise architecture, business process and data modeling with data cataloging and data literacy for risk management and digital transformation. | ACQ | ||||||||||||||||
66 | Gemini Data | Data pipeline | Data management | 0 | 2015 | Bay Area | https://www.geminidata.com/ | Gemini Data provides Data Availability for AI/ML driven analysis and applications to enable unified enterprise knowledge and access. | |||||||||||||||||
67 | Graviti | Data pipeline | Data management | Seed | 10 | 2019 | China | https://www.graviti.cn/ | Unstructured data management experts, provide AI developers and development teams with data hosting, version management, online visualization, data collaboration and other services. You can use developer tools online to integrate and use data in the cloud | ||||||||||||||||
68 | Igneous | Data pipeline | Data management | Rubrik | 67.5 | 2013 | Seattle | Acquired | https://www.igneous.io/ | Igneous Unstructured Data Protection offers the scalability to handle hundreds of file systems, billions of files, and exabytes of enterprise data requiring backup | |||||||||||||||
69 | Imply | Data pipeline | Data management | B | 45.3 | 2015 | Bay Area | https://imply.io/ | Imply delivers real-time analytics powered by Apache Druid. ... Stream or batch load data into Druid for high performance, ad-hoc analytic queries. | ||||||||||||||||
70 | Octopai | Data pipeline | Data management | B | 6.2 | 2015 | Israel | https://www.octopai.com/ | An automated, centralized, cross-platform metadata search engine that enables BI groups to quickly and precisely discover and govern shared metadata. | ||||||||||||||||
71 | Rubrik | Data pipeline | Data management | E | 553 | 2013 | Bay Area | Acquired Igneous, Opas AI | https://www.rubrik.com/en | We provide a powerful, policy-driven platform to simplify recovery and unlock insights from data residing in the data center and cloud. | |||||||||||||||
72 | Superb AI | Data pipeline | Data management | Seed | 2.3 | 2018 | Bay Area | https://www.superb-ai.com/ | Create, label and manage ML training data efficiently so you can build AI faster. Fully managed workforce. Powerful labeling tools. Training data quality control. | ||||||||||||||||
73 | Tamr | Data pipeline | Data management | 69.2 | 2012 | Boston | Raised unknown | https://www.tamr.com/ | Tamr's leading data management system and services work to create a data migration strategy that simplifies your data unification process. Talk with us today. | ||||||||||||||||
74 | Waterline Data | Data pipeline | Data management | Hitachi Vantara | 37.5 | 2013 | Bay Area | https://www.waterlinedata.com/ | Waterline's enterprise data catalog enables data professionals to discover, govern, and rationalize an organization's data lake. | ACQ | |||||||||||||||
75 | Anodot | Data pipeline | Data monitoring | C | 62.5 | 2014 | Israel | Raised 35M | https://www.anodot.com/ | We monitor your business. Anodot monitors all your data in real time for lightning fast detection of the incidents that impact your revenue | |||||||||||||||
76 | Datagrok | All-in-one | Data processing | 0 | 2019 | Pennsylvania | https://datagrok.ai/ | Datagrok: Swiss Army Knife for Data. A platform for turning data into actionable insights | |||||||||||||||||
77 | cuDF | Data pipeline | Data processing | NVIDIA | 2018 | Bay Area | https://rapids.ai/ | Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. | |||||||||||||||||
78 | Dask | Data pipeline | Data processing | Seed | 5 | 2015 | Remote | OSS | Became a startup (Coiled) | https://dask.org/ | Dask natively scales Python. Dask provides advanced parallelism for analytics, enabling performance at scale for the tools you love | ||||||||||||||
79 | Datatable | Data pipeline | Data processing | H2O | 2017 | Bay Area | OSS | https://datatable.readthedocs.io/en/latest/ | Python library for efficient multi-threaded data processing, with the support for out-of-memory datasets. | ||||||||||||||||
80 | Incorta | Data pipeline | Data processing | C | 72.6 | 2013 | Bay Area | https://incorta.com/ | Incorta aggregates large complex business data in real time, eliminating the need to reshape it. No Data Warehouse. No Transformations. Real-Time Insight. | ||||||||||||||||
81 | Koalas | Data pipeline | Data processing | Databricks | 2019 | Bay Area | https://github.com/databricks/koalas | The Koalas project makes data scientists more productive when interacting with big data, by implementing the pandas DataFrame API on top of Apache Spark. | |||||||||||||||||
82 | Modin | Data pipeline | Data processing | 0 | 2018 | OSS | https://github.com/modin-project/modin | Modin uses Ray to provide an effortless way to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code. Even using the DataFrame constructor is identical. | |||||||||||||||||
83 | Naveego | Data pipeline | Data processing | Seed | 0.5 | 2014 | Michigan | https://www.naveego.com/ | A leading provider of cloud-first, distributed data accuracy solutions for seamless, end-to-end data cleansing, Naveego enables organizations to proactively manage, detect and eliminate data accuracy issues across all enterprise data sources in real-time–regardless of structure or schema. | ||||||||||||||||
84 | Spark | Data pipeline | Data processing | Apache | 2009 | OSS | https://spark.apache.org/ | Apache Spark is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. | |||||||||||||||||
85 | Vaex | Data pipeline | Data processing | 0 | 2015 | Netherlands | OSS | https://vaex.io/ | Power up your business with our data driven solutions. With our unique, state-of-the-art technology, we provide fast and scalable solutions that will make you more agile, while limiting unnecessary resources. | Link | |||||||||||||||
86 | Amazon Redshift | Data pipeline | Data warehouse | Amazon | 2012 | Seattle | https://aws.amazon.com/redshift/ | Amazon Redshift is a fast, fully managed, and cost-effective data warehouse that gives you petabyte scale data warehousing and exabyte scale data lake analytics together in one service. Amazon Redshift is up to ten times faster than traditional on-premises data warehouses. | |||||||||||||||||
87 | Apache Hudi | Data pipeline | Data warehouse | Uber | 2016 | Bay Area | OSS | https://hudi.apache.org/ | Apache Hudi ingests & manages storage of large analytical datasets over DFS (hdfs or cloud stores) | ||||||||||||||||
88 | Delta Lake | Data pipeline | Data warehouse | Databricks | 2019 | Bay Area | OSS | https://delta.io/ | Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads. | ||||||||||||||||
89 | Yellowbrick Data | Data pipeline | Data warehouse | C | 173 | 2014 | Bay Area | https://yellowbrick.com/ | The ultimate solution for your data warehouse. Quick to deploy, easy to expand, and simple to manage. Yellowbrick Data can solve your data problems. | ||||||||||||||||
90 | Amundsen | Data pipeline | Database/Query | Lyft | 2019 | Bay Area | OSS | Amundsen is a metadata driven application for improving the productivity of data analysts, data scientists and engineers when interacting with data. | |||||||||||||||||
91 | Apache Druid | Data pipeline | Database/Query | Imply | 2012 | Bay Area | OSS | https://druid.apache.org/ | Apache Druid is a high performance real-time analytics database | ||||||||||||||||
92 | AresDB | Data pipeline | Database/Query | Uber | 2019 | Bay Area | OSS | https://github.com/uber/aresdb | A GPU-powered real-time analytics storage and query engine. | ||||||||||||||||
93 | Fluree | Data pipeline | Database/Query | Seed | 7.2 | 2017 | North Carolina | Raised 2.5M | https://flur.ee/ | Welcome to better data management. The Fluree platform organizes blockchain-secured data in a highly-scalable, highly-insightful graph database. | |||||||||||||||
94 | Hammerspace | Data pipeline | Database/Query | 0 | 2015 | Bay Area | https://hammerspace.com/ | Hammerspace allows data to move freely, like the air you breathe, across clouds and services. Make data accessible exactly where you need it, when you need it – on demand. | |||||||||||||||||
95 | Kyvos Insights | Data pipeline | Database/Query | 0 | 2015 | Bay Area | https://www.kyvosinsights.com/ | Kyvos accelerates BI on trillions of rows of data on the cloud and on-premise platforms with a semantic layer powered by its next-generation OLAP technology. | |||||||||||||||||
96 | Milvus | Data pipeline | Database/Query | Zilliz | 2019 | China | OSS | https://milvus.io/ | Milvus is an open source similarity search engine for massive feature vectors. Designed with heterogeneous computing architecture for the best cost efficiency. Searches over billion-scale vectors take only milliseconds with minimum computing resources. | ||||||||||||||||
97 | Pilosa | Data pipeline | Database/Query | Seed | 3.7 | 2017 | Texas | OSS | https://www.pilosa.com/ | Pilosa is an open source, distributed bitmap index that dramatically accelerates continuous analysis across multiple, massive data sets. | |||||||||||||||
98 | Presto | Data pipeline | Database/Query | Linux Foundation | 2012 | Bay Area | OSS | https://prestodb.io/ | Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. | ||||||||||||||||
99 | Rockset | Data pipeline | Database/Query | B | 61.5 | 2016 | Bay Area | Raised 40M | https://rockset.com/ | Rockset: The Real-Time Indexing Database in the Cloud Rockset allows you to build data-driven applications on MongoDB, DynamoDB, ... AI. Test, validate and deploy models faster by analyzing live data in real-time. | |||||||||||||||
100 | SQLFlow | Data pipeline | Database/Query | Alibaba | 2019 | China | OSS | https://sqlflow.org | Extends SQL to support AI. Extract knowledge from Data. Currently support MySQL, Apache Hive, Alibaba MaxCompute, XGBoost and TensorFlow. | ||||||||||||||||
101 | Starburst Data | Data pipeline | Database/Query | B | 64 | 2017 | Boston | Raised 42M | https://www.starburstdata.com/ | Limitless Queries. Break boundaries and harness the power of the world's fastest SQL query engine. | |||||||||||||||
102 | TerminusDB | Data pipeline | Database/Query | Seed | 1.2 | 2017 | Ireland | OSS | https://terminusdb.com/ | TerminusDB is an open source model driven graph database for knowledge graph representation designed specifically for the web-age. | |||||||||||||||
103 | Vearch | Data pipeline | Database/Query | 0 | 2019 | China | OSS | https://vearch.github.io/ | Vearch is the vector search infrastructure for deeping learning and AI applications. | ||||||||||||||||
104 | Zilliz | Data pipeline | Database/Query | B | 53 | 2017 | China | Raised 43M | https://zilliz.com/ | The company specializes in the development of open-source, AI-powered unstructured data analysis software, and is the initiator and primary contributor to the vector similarity search project Milvus. | |||||||||||||||
105 | Tecton | All-in-one | Deployment | B | 60 | 2019 | Bay Area | Raised 55M | https://tecton.ai/ | The Data Platform for Machine Learning. Build a library of great features. Serve them in production. Do it at scale. | |||||||||||||||
106 | Inferrd | Serving | Deployment | 0 | 2020 | NYC | New | https://inferrd.com/ | You build the model, we handle the deployment. Inferrd is the easiest, cheapest and the most performant hosting provider for ML models. | ||||||||||||||||
107 | Kubeflow | Serving | Deployment | 2018 | Bay Area | OSS | https://www.kubeflow.org/ | The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. | |||||||||||||||||
108 | OctoML | Serving | Deployment | A | 18.9 | 2019 | Seattle | Raised 15M | Optimize machine learning and deep learning models for deployment. From the creators of Apache TVM, XGBoost and Apache MxNet, OctoML brings the cutting edge of AI, Systems, programming languages, compilers and architecture to make machine learning systems easier to optimize and deploy. | ||||||||||||||||
109 | TensorFlow Extended | Serving | Deployment | 2019 | Bay Area | OSS | https://www.tensorflow.org/tfx | TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines | |||||||||||||||||
110 | Angel ML | Modeling & Training | Distributed | Tencent/Peking University | 2017 | Shenzhen | OSS | A Flexible and Powerful Parameter Server for large-scale machine learning | |||||||||||||||||
111 | euler | Modeling & Training | Distributed | Alibaba | 2018 | China | OSS | A distributed graph deep learning framework | |||||||||||||||||
112 | Horovod | Modeling & Training | Distributed | Uber | 2017 | Bay Area | OSS | Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use. | |||||||||||||||||
113 | Paddle | Modeling & Training | Distributed | Baidu | 2016 | China | OSS | PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice | |||||||||||||||||
114 | Ray | Modeling & Training | Distributed | UC Berkeley | 2016 | Bay Area | OSS | Ray is a fast and simple framework for building and running distributed applications. | |||||||||||||||||
115 | Grid AI | Modeling & Training | Distributed training | A | 18.6 | 2020 | NYC | Raised 18.6M, new | https://www.grid.ai/ | Seamlessly train hundreds of Machine Learning models on the cloud from your laptop. Focus on machine learning, not infrastructure. | |||||||||||||||
116 | Blaize | Hardware | Edge devices | C | 65 | 2010 | Sacramento | https://www.blaize.com/ | Intelligence at the edge of everywhere. Blaize unleashes the potential of AI to drive leaps in the value that technology delivers to transform markets and improve the way we all work and live. | ||||||||||||||||
117 | Boulder AI | Hardware | Edge devices | 0 | 2017 | Boulder | https://boulderai.com/ | Human insight and decision making on a visual sensor. | |||||||||||||||||
118 | BrainChip | Hardware | Edge devices | IPO | 27.8 | 2006 | LA | https://brainchipinc.com/ | BrainChip brings artificial intelligence to the edge with a high-performance, small, ultra-low power solution that enables continuous learning and inference. | ||||||||||||||||
119 | EdgeQ | Hardware | Edge devices | A | 53 | 2018 | Bay Area | Raised 51M, out of stealth | https://www.edgeq.io/ | EdgeQ is an information technology company that specializes in the fields of 5G chip systems. | |||||||||||||||
120 | GreenWaves Technologies | Hardware | Edge devices | A | 12.5 | 2014 | France | https://greenwaves-technologies.com/ | GreenWaves' GAP8 is the industry's first ultra-low-power processor enabling battery-operated AI in IoT applications. | ||||||||||||||||
121 | Habana Labs | Hardware | Edge devices | Intel | 75 | 2016 | Israel | https://habana.ai/ | Habana Labs was founded in 2016 to create world-class AI Processors, developed from the ground-up and optimized for training deep neural networks and for inference deployment in production environments. | ||||||||||||||||
122 | Hailo | Hardware | Edge devices | B | 87.9 | 2017 | Israel | Raised 60M | https://hailo.ai/ | The World’s Top Performing AI Processor for Edge Devices Hailo offers a breakthrough microprocessor uniquely designed to accelerate embedded AI applications on edge devices. Breathe life into your edge AI product today with Hailo-8. | |||||||||||||||
123 | Kneron | Hardware | Edge devices | A | 73 | 2015 | San Diego | Raised 40M + unknown | https://www.kneron.com/ | Kneron develops an application-specific integrated circuit and software that offers artificial intelligence-based tools. | |||||||||||||||
124 | LeapMind | Hardware | Edge devices | C | 50 | 2012 | Tokyo | https://leapmind.io/ | Ultra-low power consumption AI inference accelerator IP specialized for inference arithmetic processing of CNN that operates as a circuit on FPGA device or ASIC device . | ||||||||||||||||
125 | Mythic | Hardware | Edge devices | B | 85.2 | 2012 | Bay Area | https://www.mythic-ai.com/ | An architecture built from the ground up for AI Mythic has developed a truly unique AI compute platform that enables smart camera systems, intelligent appliances, brilliant robotics, and more. | ||||||||||||||||
126 | Prophesee | Hardware | Edge devices | C | 65.3 | 2014 | France | https://www.prophesee.ai/ | With the world’s most advanced Event-Based Vision systems, inspired by human vision and built on the foundation of neuromorphic engineering. PROPHESEE is the revolutionary system that gives Metavision to machines, revealing what was previously invisible to them. | ||||||||||||||||
127 | SiMa.ai | Hardware | Edge devices | A | 30 | 2018 | Bay Area | Raised 30M | https://sima.ai/ | Is your ML Green?TM We believe that the future of compute is high performance machine learning at the edge – and today, power is the limiter. | |||||||||||||||
128 | Syntiant | Hardware | Edge devices | C | 65.1 | 2017 | LA | Raised 35M | https://www.syntiant.com/ | Always-On Voice powered by custom AI Silicon | |||||||||||||||
129 | Zero ASIC | Hardware | Edge devices | 0 | 2020 | Boston | Changed name from Adapteva | https://www.zeroasic.com/ | Removing the Barrier to Custom Silicon | ||||||||||||||||
130 | MLFlow | All-in-one | Experiment tracking | Databricks | 2018 | Bay Area | OSS | https://mlflow.org/ | An open source platform for the machine learning lifecycle | ||||||||||||||||
131 | Allegro AI/TRAINS | Modeling & Training | Experiment tracking | A | 11 | 2016 | Israel | OSS | https://allegro.ai/ | Deep learning platform tailored for computer vision. Allegro AI offers the first end-to-end machine learning product life-cycle management solution. | |||||||||||||||
132 | Comet | Modeling & Training | Experiment tracking | A | 6.8 | 2017 | NYC | Raised 4.5M | https://www.comet.ml/ | Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects. | |||||||||||||||
133 | Datmo | Modeling & Training | Experiment tracking | One Concern | 1.2 | 2016 | Bay Area | Acqui-hired | https://www.datmo.com/ | Be as effective as AI engineers at Google and Facebook. Workflow tools to help you experiment, deploy, and scale. By data scientists, for data scientists. | |||||||||||||||
134 | Neptune | Modeling & Training | Experiment tracking | A | 4.7 | 2017 | Poland | Raised 3M | https://neptune.ai/ | All experiment-related objects relevant to your projects organized, ready to be analyzed, discussed and shared with your team. | |||||||||||||||
135 | Spell | Modeling & Training | Experiment tracking | A | 15 | 2017 | NYC | https://spell.run/ | Spell is a powerful platform for building and managing machine learning projects. Spell takes care of infrastructure, making machine learning projects easier to start, faster to get results, more organized and safer than managing infrastructure on your own. | ||||||||||||||||
136 | Tensorboard | Modeling & Training | Experiment tracking | 2015 | Bay Area | OSS | https://www.tensorflow.org/ | TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more. | |||||||||||||||||
137 | Weights & Biases | Modeling & Training | Experiment tracking | B | 20 | 2017 | Bay Area | https://www.wandb.com/ | We're building developer tools for deep learning. Add a couple lines of code to your training script and we'll keep track of your hyperparameters, system metrics, and outputs so you can compare experiments, | ||||||||||||||||
138 | DarwinAI | Modeling & Training | Explanability | Seed | 3 | 2017 | Canada | Raised unknown | https://www.darwinai.com/ | DarwinAI’s Generative Synthesis 'AI building AI' technology enables optimized and explainable deep learning. | |||||||||||||||
139 | Truera | Modeling & Training | Explanability | A | 17.3 | 2019 | Bay Area | Raised 17.3M | https://truera.com/ | The Truera Model Intelligence Platform powered by Enterprise-Class AI Explainability eliminates the machine learning black box with Model Intelligence. | |||||||||||||||
140 | Truera | Modeling & Training | Explanability | A | 17.3 | 2019 | Bay Area | Raised 17.3M | https://truera.com/ | The Truera Model Intelligence Platform powered by Enterprise-Class AI Explainability eliminates the machine learning black box with Model Intelligence. | |||||||||||||||
141 | dotData | All-in-one | Feature engineering | 43 | 2018 | Bay Area | https://dotdata.com/ | When AutoML is enhanced with AI-powered feature engineering, the result is dotData. We focus on delivering data science automation for the enterprise. End-to-end data science automation platform accelerates, democratizes, and operationalizes the entire data science process. | |||||||||||||||||
142 | FEAST | Data pipeline | Feature engineering | 0 | 2019 | Asia | OSS | https://feast.dev/ | Feast (Feature Store) is a tool for managing and serving machine learning features. Feast is the bridge between models and data. | ||||||||||||||||
143 | Boruta | Modeling & Training | Feature engineering | scikit-learn | 2010 | OSS | https://github.com/scikit-learn-contrib/boruta_py | Python implementations of the Boruta all-relevant feature selection method. | |||||||||||||||||
144 | Featuretools | Modeling & Training | Feature engineering | Alteryx | 2018 | LA | OSS | https://www.featuretools.com/ | An open source python library for automated feature engineering | ||||||||||||||||
145 | scribble Data | Modeling & Training | Feature engineering | 0 | 2016 | India | https://www.scribbledata.io/ | The feature store for your ML engineering needs | |||||||||||||||||
146 | tsfresh | Modeling & Training | Feature engineering | Blue Yonder | 2008 | Germany | OSS | https://blueyonder.com/ | Automatic extraction of relevant features from time series | ||||||||||||||||
147 | Apache ORC | Data pipeline | File format | Apache | 2013 | OSS | https://orc.apache.org/ | the smallest, fastest columnar storage for Hadoop workloads. | |||||||||||||||||
148 | Parquet | Data pipeline | File format | Twitter, Cloudera | 2013 | Bay Area | OSS | Apache Parquet is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language. | |||||||||||||||||
149 | Accord | Modeling & Training | Framework | 0 | 2012 | France | OSS | Archived | http://accord-framework.net/ | Machine learning, computer vision, statistics and general scientific computing for .NET | |||||||||||||||
150 | Alink | Modeling & Training | Framework | Alibaba | 2018 | China | OSS | Alink is the Machine Learning algorithm platform based on Flink, developed by the PAI team of Alibaba computing platform. | |||||||||||||||||
151 | Apache Mahout | Modeling & Training | Framework | Apache | 2008 | Remote | OSS | https://mahout.apache.org/ | Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end, or can be extended to other distributed backends. | ||||||||||||||||
152 | Apache MXNet | Modeling & Training | Framework | DMLC | 2015 | OSS | A flexible and efficient library for deep learning. | ||||||||||||||||||
153 | Caffe | Modeling & Training | Framework | Berkeley | 2013 | Bay Area | OSS | Caffe: a fast open framework for deep learning. | |||||||||||||||||
154 | Catalyst | Modeling & Training | Framework | 0 | 2018 | Russia | OSS | https://catalyst-team.github.io/catalyst/ | PyTorch framework for Deep Learning research and development. It focuses on reproducibility, rapid experimentation, and codebase reuse so you can create something new rather than write another regular train loop. | ||||||||||||||||
155 | Chainer | Modeling & Training | Framework | Preferred Networks | 2015 | Tokyo | OSS | https://chainer.org/ | A Powerful, Flexible, and Intuitive Framework for Neural Networks | ||||||||||||||||
156 | FedAI (FATE) | Modeling & Training | Framework | Webank | 2019 | Shenzhen | OSS | FATE (Federated AI Technology Enabler) is an open-source project initiated by Webank's AI Department to provide a secure computing framework to support the federated AI ecosystem. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). It supports federated learning architectures and secure computation of various machine learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. | |||||||||||||||||
157 | Gensim | Modeling & Training | Framework | RaRe Technologies | 2012 | Czech | OSS | https://radimrehurek.com/gensim/ | Topic Modelling for Humans. Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. | ||||||||||||||||
158 | JAX | Modeling & Training | Framework | 2018 | Bay Area | OSS | Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more | ||||||||||||||||||
159 | LightGBM | Modeling & Training | Framework | Microsoft | 2016 | Seattle | OSS | https://github.com/microsoft/LightGBM | A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. | ||||||||||||||||
160 | Ludwig | Modeling & Training | Framework | Uber | 2019 | Bay Area | OSS | http://ludwig.ai | Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. | ||||||||||||||||
161 | Mindspore | Modeling & Training | Framework | Huawei | 2020 | Shenzhen | OSS | New | MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios | ||||||||||||||||
162 | ML.NET | Modeling & Training | Framework | Microsoft | 2018 | Seattle | OSS | https://dotnet.microsoft.com/ | ML.NET is an open source and cross-platform machine learning framework for .NET | ||||||||||||||||
163 | MLlib | Modeling & Training | Framework | Spark | 2010 | OSS | https://spark.apache.org/mllib/ | MLlib is Apache Spark's scalable machine learning library. | |||||||||||||||||
164 | Pythia | Modeling & Training | Framework | 2018 | Bay Area | OSS | A modular framework for vision & language multimodal research from Facebook AI Research (FAIR) | ||||||||||||||||||
165 | PyTorch | Modeling & Training | Framework | 2015 | Bay Area | OSS | https://pytorch.org/ | Tools & Libraries. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more | |||||||||||||||||
166 | PyTorch Lightning | Modeling & Training | Framework | Grid AI | 2019 | NYC | OSS | https://www.pytorchlightning.ai/ | The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate. | ||||||||||||||||
167 | scikit-learn | Modeling & Training | Framework | 0 | 2010 | Remote | OSS | https://scikit-learn.org/stable/ | Machine Learning in Python | ||||||||||||||||
168 | TensorFlow | Modeling & Training | Framework | 2015 | Bay Area | OSS | https://www.tensorflow.org/ | An end-to-end open source machine learning platform for everyone. Discover TensorFlow's flexible ecosystem of tools, libraries and community resources | |||||||||||||||||
169 | Theano | Modeling & Training | Framework | MILA | 2008 | Canada | OSS | Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently | |||||||||||||||||
170 | Turi Create | Modeling & Training | Framework | Apple | 2018 | Bay Area | OSS | https://apple.github.io/turicreate/docs/api/ | Turi Create simplifies the development of custom machine learning models. | ||||||||||||||||
171 | XGBoost | Modeling & Training | Framework | DMLC | 2014 | OSS | XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. | ||||||||||||||||||
172 | PlaidML | Modeling & Training | Hardware compatiblity | Intel | 2017 | Bay Area | OSS | PlaidML is a framework for making deep learning work everywhere | |||||||||||||||||
173 | HyperOpt | Modeling & Training | Hyperparameter tuning | U. Waterloo | 2013 | Canada | http://hyperopt.github.io/hyperopt/ | Distributed Asynchronous Hyperparameter Optimization in Python - hyperopt/hyperopt | |||||||||||||||||
174 | Katib | Modeling & Training | Hyperparameter tuning | Kubeflow | 2018 | Bay Area | OSS | Katib is a Kubernetes Native System for Hyperparameter Tuning and Neural Architecture Search. The system is inspired by Google vizier and supports multiple ML/DL frameworks (e.g. TensorFlow, MXNet, and PyTorch). | |||||||||||||||||
175 | SigOpt | Modeling & Training | Hyperparameter tuning | Intel | 8.7 | 2014 | Bay Area | Acquired | https://sigopt.com/ | SigOpt is a standardized, scalable, enterprise-grade optimization platform and API designed to unlock the potential of your modeling pipelines. | |||||||||||||||
176 | talos | Modeling & Training | Hyperparameter tuning | Autonomio | 2018 | Finland | OSS | https://autonom.io | Hyperparameter Optimization for TensorFlow, Keras and PyTorch | ||||||||||||||||
177 | Tune | Modeling & Training | Hyperparameter tuning | Ray | 2017 | Bay Area | https://ray.readthedocs.io/en/latest/tune.html | Tune is a Python library for hyperparameter tuning at any scale. | |||||||||||||||||
178 | Apache TVM | Serving | Inference | Apache | 2017 | OSS | Apache TVM (incubating) is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends. | ||||||||||||||||||
179 | MNN | Serving | Inference | Alibaba | 2019 | China | OSS | MNN is a lightweight deep neural network inference engine. | |||||||||||||||||
180 | TensorRT | Serving | Inference | NVIDIA | 2019 | Bay Area | OSS | NVIDIA TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. | |||||||||||||||||
181 | explainX.ai | Modeling & Training | Interpretability | Pre-seed | 0 | 2020 | NYC | New | https://www.explainx.ai/ | ExplainX enables you to explain, present, and monitor how your AI models work. We make sure your models never fail in the real-world. | |||||||||||||||
182 | Fiddler Labs | Modeling & Training | Interpretability | A | 13.2 | 2018 | Bay Area | Raised unknown | https://www.fiddler.ai/ | AI with trust, visibility, and insightts built in. Fiddler is a breakthrough AI engine with explainability at its heart. | |||||||||||||||
183 | Fiddler Labs | Modeling & Training | Interpretability | A | 13.2 | 2018 | Bay Area | Raised unknown | https://www.fiddler.ai/ | AI with trust, visibility, and insightts built in. Fiddler is a breakthrough AI engine with explainability at its heart. | |||||||||||||||
184 | InterpretML | Modeling & Training | Interpretability | Microsoft | 2019 | Seattle | OSS | Fit interpretable machine learning models. Explain blackbox machine learning | |||||||||||||||||
185 | Kyndi | Modeling & Training | Interpretability | B | 20 | 2019 | Bay Area | Raised 20M | www.kyndi.com | ||||||||||||||||
186 | LIME | Modeling & Training | Interpretability | University of Washington | 2016 | Seattle | OSS | Lime: Explaining the predictions of any machine learning classifier | |||||||||||||||||
187 | Lucid | Modeling & Training | Interpretability | 2018 | Bay Area | OSS | A collection of infrastructure and tools for research in neural network interpretability | ||||||||||||||||||
188 | SHAP | Modeling & Training | Interpretability | University of Washington | 2017 | Seattle | OSS | A game theoretic approach to explain the output of any machine learning model. | |||||||||||||||||
189 | HIVE | All-in-one | Labeling | B | 20.2 | 2013 | Bay Area | https://thehive.ai/ | Hive is a full-stack deep learning company focused on solving visual intelligence problems. Let us help you join the AI Revolution. End-To-End Solutions. Full-Stack Approach. | ||||||||||||||||
190 | Dataturks | Data pipeline | Labeling | Walmart | 2018 | India | https://dataturks.com/ | ML data annotations made super easy for teams. Just upload data, add your team and build training/evaluation dataset in hours. | ACQ | ||||||||||||||||
191 | Doccano | Data pipeline | Labeling | 0 | 2018 | Tokyo | OSS | https://doccano.herokuapp.com/ | Text annotation for Human. Just create project, upload data and start annotation. You can build dataset in hours. | ||||||||||||||||
192 | Figure Eight | Data pipeline | Labeling | Appen | 300 | 2008 | Bay Area | https://www.figure-eight.com/ | Figure Eight combines the best of human and machine intelligence to provide high-quality annotated training data that powers the world's most innovative machine learning and business solutions | ACQ | |||||||||||||||
193 | Heartex Label Studio | Data pipeline | Labeling | Seed | 0.15 | 2018 | Bay Area | OSS | https://www.heartex.ai/ | Label Studio is a multi-type data labeling and annotation tool with standardized output format | |||||||||||||||
194 | iMerit | Data pipeline | Labeling | B | 23.5 | 2012 | Bay Area | Raised 20M | https://imerit.net/ | iMerit specializes in data labeling and annotation for purposes of training models for Machine Learning and Artificial Intelligence. | |||||||||||||||
195 | Labelbox | Data pipeline | Labeling | B | 42.8 | 2018 | Bay Area | Raised 25M | https://labelbox.com/ | A complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features. | |||||||||||||||
196 | LabelImg | Data pipeline | Labeling | 0 | 2016 | Canada | OSS | https://github.com/tzutalin/labelImg | LabelImg is a graphical image annotation tool and label object bounding boxes in images | ||||||||||||||||
197 | Playment | Data pipeline | Labeling | Seed | 2.5 | 2015 | India | https://playment.io/ | Build high-quality ground truth datasets with ML-assisted tools, sophisticated project management software, expert human workforce, and much more. | ||||||||||||||||
198 | Prodigy | Data pipeline | Labeling | Explosion AI | 2017 | Germany | https://prodi.gy/ | Prodigy is a scriptable annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration. ... With Prodigy you can take full advantage of modern machine learning by adopting a more agile approach to data collection. | |||||||||||||||||
199 | Segments.ai | Data pipeline | Labeling | 0 | 2020 | Belgium | New | https://segments.ai/ | Deep learning-fueled labeling technology with a focus on instance and semantic segmentation. | ||||||||||||||||
200 | Snorkel | Data pipeline | Labeling | Snorkel AI | 2016 | Bay Area | OSS | https://snorkel.ai | Programmatically Building and Managing Training Data | ||||||||||||||||
201 | V7Labs | Data pipeline | Labeling | Seed | 3 | 2018 | UK | Raised 3M | https://www.v7labs.com/ | Create the Sense of Sight Label, train, and deploy artificial intelligence that effortlessly learns new objects from your data. | |||||||||||||||
202 | Voxel51 // Scoop | Data pipeline | Labeling | Seed | 3.3 | 2018 | Michigan | https://voxel51.com/scoop/ | We build software that enables ML engineers to build better models, more quickly. Try FiftyOne, our powerful platform for dataset curation, analysis, and model | ||||||||||||||||
203 | Core ML | Serving | Mobile | Apple | 2017 | Bay Area | Use Core ML to integrate machine learning models into your app. Core ML provides a unified representation for all models. | ||||||||||||||||||
204 | Fritz AI | Serving | Mobile | A | 7 | 2017 | Boston | Raised 5M | https://www.fritz.ai/ | Fritz AI is the machine learning platform for iOS and Android developers. Teach your mobile apps to see, hear, sense, and think. | |||||||||||||||
205 | ML Kit | Serving | Mobile | 2018 | Bay Area | OSS | ML Kit beta brings Google's machine learning expertise to mobile developers in a powerful and easy-to-use package. | ||||||||||||||||||
206 | ncnn | Serving | Mobile | Tencent | 2017 | Shenzhen | OSS | ncnn is a high-performance neural network inference framework optimized for the mobile platform | |||||||||||||||||
207 | TensorFlow Lite | Serving | Mobile | 2019 | Bay Area | OSS | https://www.tensorflow.org/lite | TensorFlow Lite is an open source deep learning framework for on-device inference. | |||||||||||||||||
208 | AIMET | Modeling & Training | Model compression | Qualcomm | 2020 | San Diego | OSS | New | https://github.com/quic/aimet | AIMET is a library that provides advanced quantization and compression techniques for trained neural network models. | |||||||||||||||
209 | Deeplite | Serving | Model compression | 0 | 2020 | Canada | New | https://www.deeplite.ai/ | Enabling faster, smaller and more energy-efficient DNNs to run on edge devices and in the cloud | ||||||||||||||||
210 | Neural Network Distiller | Serving | Model compression | Intel | 2018 | Bay Area | OSS | Distiller is an open-source Python package for neural network compression research. Network compression can reduce the memory footprint of a neural network, increase its inference speed and save energy. Distiller provides a PyTorch environment for prototyping and analyzing compression algorithms, such as sparsity-inducing methods and low-precision arithmetic. | |||||||||||||||||
211 | Xnor.ai | Serving | Model compression | Apple | 14.6 | 2016 | Seattle | Acquired 200M | http://xnor.ai/ | Transform your business with on-device AI. | ACQ | ||||||||||||||
212 | Dessa | All-in-one | Monitoring | Square | 9 | 2016 | Canada | Acquired | https://www.dessa.com/ | Create more with machine learning. Build, run & monitor 1000s of ML experiments with Foundations | ACQ | ||||||||||||||
213 | Prometheus | Data pipeline | Monitoring | Apache/PromLabs | 2012 | Germany | OSS | https://prometheus.io/ | An open-source monitoring system with a dimensional data model, flexible query language, efficient time series database and modern alerting approach. | ||||||||||||||||
214 | Arize AI | Serving | Monitoring | Seed | 4 | 2019 | Bay Area | https://arize.com/ | Arize AI is the watcher, troubleshooter and the guardrail on deployed AI | ||||||||||||||||
215 | Arthur AI | Serving | Monitoring | A | 18.3 | 2018 | NYC | Raised 15M | https://www.arthur.ai/ | Always-on Explainability, Bias, and Performance Monitoring for AI, ML, and analytics. Get up and running in minutes and start sleeping better at night. Dedicated. Innovative. | |||||||||||||||
216 | Datatron | Serving | Monitoring | Seed | 7.8 | 2016 | Bay Area | Raised 1.4M | https://www.datatron.com/ | Production AI Model Management at Scale. Automate the standardized deployment, monitoring, governance, and validation of all your models to be developed in any environment. | |||||||||||||||
217 | Evidently AI | Serving | Monitoring | 0 | 2020 | Russia | OSS | New | https://evidentlyai.com/ | Open-source tools to analyze, monitor, and debug machine learning model in production | |||||||||||||||
218 | Losswise | Serving | Monitoring | Mathpix | 2017 | Bay Area | https://losswise.com/ | Turn your GPUs into monitored build servers from a git push with Losswise. Interactive visualization, logs, smart notifications, and more. Start free today. | |||||||||||||||||
219 | Mona Labs | Serving | Monitoring | Seed | 3.9 | 2018 | Atlanta | Raised 3.9M | https://www.monalabs.io/ | PRODUCTION MONITORING FOR AI. With Mona, you gain complete transparency into how your data and models behave in the real world. | |||||||||||||||
220 | superwise.ai | Serving | Monitoring | Seed | 4.5 | 2019 | Israel | Raised 4.5M | https://superwise.ai | Monitor your AI from the moment it meets reality so you can finally trust every model | |||||||||||||||
221 | Unravel Data | Serving | Monitoring | C | 57.2 | 2013 | Bay Area | https://unraveldata.com/ | Unravel provides full-stack visibility and AI-powered guidance to help you understand and optimize the performance of your data-driven applications. | AppDynamics, New Relic, Splunk, and DataDog | |||||||||||||||
222 | VertaAI | Serving | Monitoring | A | 10 | 2019 | Bay Area | Raised 10M | https://www.verta.ai/ | Verta.AI is a Palo Alto-based startup building software infrastructure to help enterprise data science and machine learning (ML) teams rapidly develop and deploy ML models. | |||||||||||||||
223 | AllenNLP | Modeling & Training | NLP | Incubator | 2016 | Seattle | OSS | https://allennlp.org/ | AllenNLP is an open-source NLP research library, built on PyTorch. | ||||||||||||||||
224 | fastText | Modeling & Training | NLP | 2016 | Bay Area | OSS | Library for fast text representation and classification. | ||||||||||||||||||
225 | flair | Modeling & Training | NLP | Zalando | 2018 | Germany | OSS | A very simple framework for state-of-the-art Natural Language Processing (NLP) | |||||||||||||||||
226 | Hugging Face | Modeling & Training | NLP | A | 20.2 | 2016 | NYC | https://huggingface.co/ | We're on a journey to solve and democratize artificial intelligence through natural language. | ||||||||||||||||
227 | OpenSeq2Seq | Modeling & Training | NLP | NVIDIA | 2017 | Bay Area | OSS | Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP | |||||||||||||||||
228 | spaCy | Modeling & Training | NLP | Explosion AI | 0 | 2014 | Germany | OSS | https://spacy.io/ | spaCy is a free open-source library for Natural Language Processing in Python. It features NER, POS tagging, dependency parsing, word vectors and more. | |||||||||||||||
229 | Dialogflow | Modeling & Training | NLU | 2014 | Bay Area | https://dialogflow.com/ | Dialogflow is a Google service that runs on Google Cloud Platform, letting you scale to hundreds of millions of users. Optimized for the Google Assistant. | ||||||||||||||||||
230 | NeMo | Modeling & Training | NLU | NVIDIA | 2019 | Bay Area | OSS | NeMo: a toolkit for conversational AI | |||||||||||||||||
231 | Rasa | Modeling & Training | NLU | B | 40.1 | 2016 | Germany | OSS | Raised 26M | https://rasa.com/ | Build contextual AI assistants and chatbots in text and voice with our open source machine learning framework. Scale it with our enterprise grade platform. | ||||||||||||||
232 | Colab | Modeling & Training | Notebook | 2017 | Bay Area | https://colab.research.google.com/ | Colab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX and more. | ||||||||||||||||||
233 | DeepNote | Modeling & Training | Notebook | Seed | 3.8 | 2019 | Bay Area | Raised 3.8M | https://deepnote.com/ | The notebook you’ll love to use Deepnote is a new kind of data science notebook. Jupyter-compatible with real-time collaboration and easy deployment. Oh, and it's free. | |||||||||||||||
234 | nteract | Modeling & Training | Notebook | Nteract | 2015 | Texas | OSS | https://nteract.io/ | nteract is an open-source organization committed to creating fantastic interactive computing experiences that allow people to collaborate with ease. We build SDKs, applications, and libraries that help you and your team make the most of interactive (particularly Jupyter) notebooks and REPLs. | ||||||||||||||||
235 | papermill | Modeling & Training | Notebook | Nteract | 2017 | Texas | OSS | https://papermill.readthedocs.io/en/latest/ | Papermill is a tool for parameterizing and executing Jupyter Notebooks. | ||||||||||||||||
236 | River | Modeling & Training | Online learning | 0 | 2017 | France | OSS | Changed name from Creme | https://riverml.xyz/ | A Python package for online/streaming machine learning. | |||||||||||||||
237 | Vowpal Wabbit | Modeling & Training | Online learning | Microsoft | 2010 | OSS | https://vowpalwabbit.org/ | Vowpal Wabbit provides a fast, flexible, online, and active learning solution that empowers you to solve complex interactive machine learning problems | |||||||||||||||||
238 | BentoML | Modeling & Training | Pretrained models | 0 | 2018 | Bay Area | OSS | https://bentoml.com/ | BentoML makes it easy to serve and deploy machine learning models in the cloud. It is an open source framework for building cloud-native model serving services. BentoML supports most popular ML training frameworks and deployment platforms, including major cloud providers and docker/kubernetes. | ||||||||||||||||
239 | Dockship | Modeling & Training | Pretrained models | 0 | 2019 | India | https://dockship.io/ | Dockship.io is a marketplace for AI models and datasets. Publish your models on Dockship for people all over the world. | |||||||||||||||||
240 | GluonCV | Modeling & Training | Pretrained models | Microsoft | 2018 | Seattle | OSS | https://gluon-cv.mxnet.io/ | GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It aims to help engineers, researchers, and students quickly prototype products, validate new ideas and learn computer vision. | ||||||||||||||||
241 | Aircloak | Data pipeline | Privacy | 1.3 | 2012 | Germany | https://aircloak.com/ | Aircloak's unique approach ensures the existing primary database is not modified in any way. Aircloak handles all data types including unstructured text. | |||||||||||||||||
242 | Gretel AI | Data pipeline | Privacy | A | 15.5 | 2019 | San Diego | Raised 15.5M | https://gretel.ai/ | The first and only APIs to enable you to balance, anonymize, and share your data. With privacy guarantees. | |||||||||||||||
243 | Tumult Labs | Data pipeline | Privacy | 0 | 2019 | North Carolina | https://www.tmlt.io/ | Unleashing the power of data with ironclad privacy protection | |||||||||||||||||
244 | PySyft | Modeling & Training | Privacy | OpenMined | 2017 | UK | OSS | PySyft is a Python library for secure and private Deep Learning. PySyft decouples private data from model training, using Federated Learning, Differential Privacy, and Multi-Party Computation (MPC) within the main Deep Learning frameworks like PyTorch and TensorFlow. | |||||||||||||||||
245 | Pyro | Modeling & Training | Programming language | Uber | 2017 | Bay Area | OSS | Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch | |||||||||||||||||
246 | Robust AI | All-in-one | Robotics | A | 22.5 | 2019 | Bay Area | Raised 15M | https://www.robust.ai/ | Robust.AI: Creating a New Foundation for the Future of Robotics. | |||||||||||||||
247 | Formant | Serving | Robotics | A | 6 | 2019 | Bay Area | https://formant.io/ | Deploy faster. Improve uptime. Achieve scale. | ||||||||||||||||
248 | Aible | All-in-one | Serving | 26.4 | 2018 | Bay Area | Raised 26.4M | https://www.aible.com/ | Create AI that delivers impact, not accuracy, with cost-benefit tradeoffs & operational constraints, in a friendly, intuitive UI designed for real business. | ||||||||||||||||
249 | Polyaxon | All-in-one | Serving | 0 | 2016 | Germany | OSS | https://polyaxon.com/ | A platform for reproducing and managing the whole life cycle of machine learning and deep learning applications. | ||||||||||||||||
250 | Algorithmia | Serving | Serving | B | 37.9 | 2013 | Seattle | https://algorithmia.com/ | Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone | ||||||||||||||||
251 | Seldon | Serving | Serving | A | 13.7 | 2011 | UK | OSS | Raised 9.4M | https://www.seldon.io/ | Manage, serve and scale models built in any framework on Kubernetes. Take your ML projects from POC to production. | ||||||||||||||
252 | ClearSky Data | Data pipeline | Storage | B | 59 | 2014 | Boston | https://www.clearskydata.com/ | ClearSky Data offers enterprise storage as a hybrid cloud service delivering on-demand primary storage, offsite backup, and DR as a single service. | ||||||||||||||||
253 | Datera | Data pipeline | Storage | C | 63.9 | 2013 | Bay Area | https://datera.io/ | Get sub-200µS latency & millions of IOPS with 100% software-defined data automation. Save up to 70% on data infrastructure total-cost-of-ownership. | ||||||||||||||||
254 | Elastifile | Data pipeline | Storage | 74 | 2013 | Bay Area | https://www.elastifile.com/ | Elastifile's cloud-native file storage helps organizations adapt and accelerate their business in the cloud era. Powered by a scalable, enterprise-grade distributed file system with intelligent object tiering, Elastifile augments existing public cloud services with a scalable, POSIX-compliant NAS, facilitating frictionless cloud adoption. With Elastifile, organizations enjoy low-touch file storage services, or deploy and manage cloud-native file storage themselves, eliminating the need for manual storage management and IT forecasting. Elastifile's unique combination of features and flexibility empowers organizations to seamlessly integrate cloud resources, with no application refactoring… thereby modernizing their infrastructure and achieving IT agility and efficiency goals. | ACQ | ||||||||||||||||
255 | Excelero | Data pipeline | Storage | B | 35 | 2014 | Bay Area | https://www.excelero.com/ | Local NVMe performance at data center scale through true convergence. Software-defined block storage for Cloud and Enterprise applications at any scale. | ||||||||||||||||
256 | Komprise | Data pipeline | Storage | C | 50.7 | 2014 | Bay Area | https://www.komprise.com/ | In 15 minutes, our free data management software trial will show you how you can save 70% on data management costs, on-premises and in the cloud. | ||||||||||||||||
257 | Quobyte | Data pipeline | Storage | B | 0 | 2013 | Germany | https://www.quobyte.com/ | Quobyte is software defined storage that turns commodity servers into a reliable and highly automated data center file system. | ||||||||||||||||
258 | Storbyte | Data pipeline | Storage | 0 | 2014 | DC | http://storbyte.com/ | Storbyte designs and manufactures all-flash & hybrid flash enterprise storage arrays that offer performance, power management, availability, reliability, density, efficiency, flexibility, expandability, and affordability. Storbyte is providing innovative data storage solutions and has not lost sight of what is important to end users: a responsible, cost-correct price point. | |||||||||||||||||
259 | Vexata | Data pipeline | Storage | StorCentric | 54 | 2014 | Bay Area | https://www.vexata.com/ | Vexata is an active data infrastructure company that accelerates database and analytic platforms via groundbreaking storage solutions. | ACQ | |||||||||||||||
260 | Confluent | Data pipeline | Stream processing | E | 455.9 | 2014 | Bay Area | Raised 250M, hired an IPO CFO | https://www.confluent.io/ | Confluent is a fully managed Kafka service and enterprise stream processing platform. Real-time data streaming for AWS, GCP, Azure or serverless. Try free! | |||||||||||||||
261 | Materialize | Data pipeline | Stream processing | B | 40.5 | 2019 | NYC | Raised 32M | https://materialize.com/ | Materialize delivers SQL exploration for streaming events and real-time data. Incrementally updated materialized views - in ANSI Standard SQL and in real-time. Micro-batching. | |||||||||||||||
262 | Apache Flink | Serving | Stream processing | Apache | 2011 | Germany | OSS | Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities | |||||||||||||||||
263 | Apache Kafka | Serving | Stream storage | Apache | 2011 | Bay Area | OSS | Apache Kafka is an open-source stream-processing software platform developed by LinkedIn and donated to the Apache Software Foundation, written in Scala and Java. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. | |||||||||||||||||
264 | Company | Cat | SubCat | Series | $$$ (M) | Started | HQ | OSS | Change in 2020 | Website | Description | IF ACQ | |||||||||||||
265 | Dolt | Data pipeline | Versioning | Seed | 2 | 2018 | LA | https://www.liquidata.co/ | Liqiudata's mission is to make data move more efficiently. We built Dolt, an an open-source version-controlled SQL database with Git-like semantics. | ||||||||||||||||
266 | DVC - Iterative.ai | Data pipeline | Versioning | 3.9 | 2017 | Bay Area | OSS | https://dvc.org/ | Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments. | ||||||||||||||||
267 | Git LFS | Data pipeline | Versioning | Atlassian, GitHub | 2014 | Remote | OSS | https://git-lfs.github.com/ | Git Large File Storage (LFS) replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git, while storing the file contents on a remote server like GitHub.com or GitHub Enterprise. | ||||||||||||||||
268 | Pachyderm | Data pipeline | Versioning | B | 28.1 | 2014 | Bay Area | OSS | Raised 16M | https://www.pachyderm.com/ | Data Lineage with End-to-End Pipelines on Kubernetes, engineered for the enterprise. And… It's open source! | ||||||||||||||
269 | Qri | Data pipeline | Versioning | 0 | 2016 | NYC | OSS | https://qri.io/ | Bigger than a spreadsheet, smaller than a database, datasets are all around us. Use Qri to browse, download, create, fork, & publish datasets across a network of peers. | ||||||||||||||||
270 | Quilt Data | Data pipeline | Versioning | Seed | 4.2 | 2015 | Bay Area | OSS | https://quiltdata.com/ | Quilt is a versioned data portal for AWS | |||||||||||||||
271 | DAGsHub | Modeling & Training | Versioning | Seed | 3 | 2019 | Israel | Raised 3M | https://dagshub.com/ | DAGsHub is a platform for data version control and collaboration for data scientists and machine learning engineers. | |||||||||||||||
272 | Replicate | Modeling & Training | Versioning | 0 | 2020 | Bay Area | OSS | New | https://replicate.ai | Version control for machine learning | |||||||||||||||
273 | PerceptiLabs | Modeling & Training | Visual modeling | Seed | 2 | 2019 | Bay Area | https://perceptilabs.readme.io/ | PerceptiLabs takes the process of building and training a machine learning model to warp speed. We not only accelerate machine learning, we advance explainability in AI | ||||||||||||||||
274 | Facets | Data pipeline | Visualization | 2017 | Bay Area | OSS | https://github.com/PAIR-code/facets | Facets: An Open Source Visualization Tool for Machine Learning Training Data | |||||||||||||||||
275 | Gluent | Data pipeline | Visualization | Seed | 8.2 | 2014 | Texas | https://gluent.com/ | Data virtualization software eliminates data silos. Gluent's transparent data virtualization provides virtual access to all enterprise data, with zero code changes. | ||||||||||||||||
276 | Netron | Modeling & Training | Visualization | 0 | 2011 | Seattle | OSS | https://netron.app/ | Netron is a viewer for neural network, deep learning and machine learning models. | ||||||||||||||||
277 | Clipper | Serving | Web | Berkeley | 2017 | Bay Area | OSS | http://clipper.ai/ | Clipper is a low-latency prediction serving system for machine learning. Clipper makes it simple to integrate machine learning into user-facing serving systems. | ||||||||||||||||
278 | Cortex | Serving | Web | 0 | 2019 | Bay Area | OSS | https://www.cortex.dev/ | Cortex is an open source platform for deploying machine learning models as production web services. | ||||||||||||||||
279 | PredictionIO | Serving | Web | Salesforce | 2013 | Bay Area | OSS | https://predictionio.apache.org/ | Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture. | ||||||||||||||||
280 | Elementl | All-in-one | Workflow orchestration | 1.8 | 2018 | Bay Area | OSS | Raised 1.8M | https://www.elementl.com/ | Building Dagster, the data orchestrator. Dagster is a data orchestrator for machine learning, analytics, and ETL | |||||||||||||||
281 | Michelangelo | All-in-one | Workflow orchestration | Uber | 2015 | Bay Area | Michelangelo, Uber’s machine learning (ML) platform, supports the training and serving of thousands of models in production across the company. Designed to cover the end-to-end ML workflow, the system currently supports classical machine learning, time series forecasting, and deep learning models that span a myriad of use cases ranging from generating marketplace forecasts, responding to customer support tickets, to calculating accurate estimated times of arrival (ETAs) and powering our One-Click Chat feature using natural language processing (NLP) models on the driver app. | ||||||||||||||||||
282 | Airflow | Infrastructure | Workflow orchestration | Airbnb | 2015 | Bay Area | OSS | https://airflow.apache.org/ | Airflow is a platform created by community to programmatically author, schedule and monitor workflows. | ||||||||||||||||
283 | Backend AI | Infrastructure | Workflow orchestration | 0 | 2016 | Seoul | OSS | https://www.backend.ai | Backend.AI: Minute-made GPU clustering solution for Machine Learning. | ||||||||||||||||
284 | Cadence | Infrastructure | Workflow orchestration | Uber | 2017 | Bay Area | OSS | Cadence is a distributed, scalable, durable, and highly available orchestration engine to execute asynchronous long-running business logic in a scalable and resilient way. | |||||||||||||||||
285 | Flyte | Infrastructure | Workflow orchestration | Lyft | 2019 | Bay Area | OSS | Prepared to become a startup | https://flyte.org/ | Lyft’s Cloud Native Machine Learning and Data Processing Platform, Now Open Sourced | |||||||||||||||
286 | Luigi | Infrastructure | Workflow orchestration | Spotify | 2012 | Sweden | OSS | Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in. | |||||||||||||||||
287 | Metaflow | Infrastructure | Workflow orchestration | Netflix | 2019 | Bay Area | OSS | https://metaflow.org/ | Metaflow makes it quick and easy to build and manage real-life data science projects. Metaflow is built for data scientists, not just for machines. | metaflow.org | |||||||||||||||
288 | Prefect | Infrastructure | Workflow orchestration | A | 14.1 | 2018 | DC | OSS | Raised 11.6M | https://www.prefect.io/ | The Global Leader in Dataflow Automation | ||||||||||||||
289 | Valohai | Infrastructure | Workflow orchestration | A | 1.8 | 2016 | Finland | https://valohai.com/ | The MLOps platform for the whole team. Valohai takes you from POC to production while managing the whole model lifecycle. |
A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | AA | AB | AC | AD | AE | AF | AG | AH | AI | AJ | AK | AL | AM | AN | AO | AP | AQ | AR | AS | AT | AU | AV | AW | AX | AY | AZ | BA | BB | BC | BD | BE | BF | BG | BH | BI | BJ | BK | BL | BM | BN | BO | BP | BQ | BR | BS | BT | BU | BV | |
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2 | Product | Type | Owner | Linkedin URL | Py/IPynb Client Git Repo | Main Git Repo | Product Page URL | Company URL | PyPI URL | pip install <name> | Slack / Community Join URL | Forum URL | Founded | Size | Location | License | Individual Price | Team Price | Startup price | Enterprise Price | Storage Plans | Free storage | Issues Flags (see tab for definitions) | Lahyani Thoughts | User Gen'd IDs Required | Autogenerated IDs | Implementation Type | Abstractions | Viewed | Evaluated | Contacts? | Business Model | Server Language | Languages | Notebook Tutorials | Open Source Client | Open Source Server | Cloud Repo Included | Built in UI | Training Data Dev | Dataset Versioning | One-Click Reproducible Checkpoints | Experiments Tracking (HP Tuning, etc..) | Training Resource Monitoring | Training Neural Model Debugging | Neural Model Training Failure Modes Tracking | Training Forensics | Prod Resource Monitoring | Feature Store (TODO) | Model Metric based Model Comparison | FALSE | Live Implemented Model Comparsion (A/B) | Configured KPIs/Enforced Benchmarks | Training scheduler (e.g. Sweeps, Ray Tune, etc..) | Training runtime | Model Serving | Real-time model serving | REST API serving | Authenticated/Metered Usage on Model Serving | Apps/Visualizations Framework Integrated with Models | 0 downtime production model versioning | Vertical Collaboration (competition) | Horizontal Collaboration (cooperation) | Outsider Collaboration (reports) | Model Zoo | Source | Alias1 | Alias2 | ||||||
3 | DVC | DataOps-ML | Iterative.ai | https://dvc.org/ | dvc | 2018 | 11-50 | SF USA | Apache 2.0 | 0 | 0 | 0 | 0 | CLI, NoMod | TrainingSet, MLPipeline | TRUE | FALSE | FALSE | Open Source | Python, Agnostic | TRUE | TRUE | FALSE | FALSE; Tensorboard | TRUE | TRUE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE | DVC - Iterative.ai | ||||||||||||||||||||||||||||||||||
4 | CML | CI/CD | Iterative.ai | https://cml.dev/ | 2018 | 11-50 | SF USA | Apache 2.0 | 0 | 0 | 0 | 0 | CLI, NoMod | TRUE | FALSE | FALSE | Open Source | Python, Agnostic | TRUE | TRUE | FALSE | FALSE; Tensorboard | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE | TRUE (CICD) | Maybe | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | |||||||||||||||||||||||||||||||||||||
5 | Kubeflow | End-to-End | Kubeflow | none | 2017 | 51-200 | SF USA | Apache 2.0 | 0 | 0 | 0 | 0 | Stand-alone | FALSE | FALSE | TRUE | Open Source | Kubeflow | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
6 | Pachyderm | End-to-End,DataOps-Analytics | Pachyderm | none | 2014 | 11-50 | SF USA | Pachyderm Community License | 0 | 0 | 0 | 0 | CLI, *nix, NoMod,PyContext,PyFunctions | DataPipeline, Repo, Input, Transform, | TRUE | FALSE | TRUE | Open Source | Go | Python, CLI | TRUE | TRUE | FALSE | TRUE | FALSE | TRUE | FALSE | Pachyderm | ||||||||||||||||||||||||||||||||||||||||||||||
7 | git-lfs | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
8 | Label Studio | DataOps-Labeling | Heartex Labs | https://www.linkedin.com/company/heartex/ | none | 2018 | 2-10 | SF USA | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | FALSE | FALSE | Open Source | Python/Flask;React/MST | Aaron Soellinger | Heartex Label Studio | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
9 | Seldon | Deployement | Alex Housley | https://www.linkedin.com/company/seldon/ | none | 2014 | 2-10 | LONDON UK | Apache 2.0 | 0 | 0 | 0 | 0 | Open Source | TRUE | TRUE | TRUE | Seldon | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
10 | Weights and Biases | Experiment Monitoring | Weights and Biases | closed | none | 2017 | 11-50 | SF USA | Commercial | free | $200/u/mo | $35/u/mo | not published | TRUE | 100GB | Supported | TRUE | CLI, PyCallbacks, PyFunctions | Experiment, Model, Dataset, Sweep, Agent, Report, Visualization | TRUE | TRUE | TRUE | Closed Source, Freemium + Enterprise | Python | TRUE | TRUE | FALSE | TRUE | TRUE | FALSE | TRUE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | TRUE | TRUE | TRUE | Weights & Biases | |||||||||||||||||||||||||||||
11 | Verta AI | Experiment Monitoring | VertaAI | https://www.linkedin.com/company/vertaai/ | https://verta.ai/ | none | 2018 | 2-10 | Palo Alto, CA | Apache 2.0 | free | none | not published | not published | FASLE | none | PyCallback | Experiment | TRUE | TRUE | TRUE | Open Source | Python | TRUE | TRUE | TRUE | FALSE | TRUE | FALSE | TRUE | FALSE | TRUE | Pay-Only | Pay-Only | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE | FALSE | TRUE | VertaAI | ||||||||||||||||||||||||||||||
12 | Comet ML | Experiment Monitoring | Comet ML | closed | none | none | 2017 | 11-50 | NY USA | none | free | $179/u/mo | $39/u/mo | not published | TRUE | 100GB | Project Name | FALSE | PyFunction | Experiment | TRUE | FALSE | FALSE | Open with Closed Source Fork, Freemium + Enterprise | Python, Java, R, REST | TRUE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | PAID | FALSE | FALSE | FALSE | TRUE | TRUE | TRUE | Comet | |||||||||||||||||||||||||||||||
13 | ClearML | End-to-end | Allegro AI | https://www.linkedin.com/company/clearml | https://clear.ml | none | 2016 | 11-50 | Israel | Apache 2.0 | free | free | none | not published | TRUE | 100GB (Clear ML plan) | Experiment, Model, Dataset, HPO, Agent, Report, Visualization, agent-services, task | FALSE | FALSE | TRUE | Open Source | Python | Python | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | |||||||||||||||||||||||||||||||||||||
14 | Valohai | End-to-End | Valohai | https://www.linkedin.com/company/valohai/ | none | none | 2016 | 2-10 | SF USA, Turku Finland | none | none | not published | none | not published | FALSE | none | NoMod | Step, Input, (Hyper)Parameter, | TRUE | FALSE | FALSE | Closed Source | Valohai | |||||||||||||||||||||||||||||||||||||||||||||||||||
15 | ML Flow | End-to-End | LF Projects llc (ml-flow), Databricks | None | none | none | None | DE USA | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | CLI, PyContext | Tracking, Project, Model, Model Registry | TRUE | FALSE | FALSE | Open Source | Python, Java, Scala, R | TRUE | TRUE | FALSE | TRUE | FALSE | FALSE | FALSE | TRUE | FALSE | TRUE | FALSE | FALSE | TRUE | TRUE | TRUE | FALSE | TRUE | FALSE | TRUE | MLFlow | ||||||||||||||||||||||||||||||||
16 | Neptune.ai | Experiment Monitoring | Neptune ML | closed | 2017 | Warsaw Poland | Experiment Name, Experiment ID | Unknown | CLI, PyCallbacks, PyFunctions | TRUE | FALSE | TRUE | Closed Source, Freemium + Enterprise | Python, R | TRUE | Basic | TRUE-psutil | FALSE | FALSE | TRUE | TRUE | Jakub Czakon | Neptune | |||||||||||||||||||||||||||||||||||||||||||||||||||
17 | Guild AI | Experiment Monitoring | Guild AI | https://www.linkedin.com/company/guildai/ | https://guild.ai | none | none | 1 | Chicago USA | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | CLI, NoMod, | TRUE | FALSE | FALSE | Open Source | Python | TRUE | TRUE | FALSE | TRUE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | Maybe | TRUE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | Grid AI | ||||||||||||||||||||||||||||||||
18 | Spell.ML | End-to-End | Spell | None | https://spell.ml/ | none | 2017 | 11-50 | NY USA | free | $329/u/mo | 0 | not published | FALSE | none | TRUE | FALSE | FALSE | Closed Source | Python | TRUE | FALSE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE | TRUE | TRUE | TRUE | FALSE | FALSE | TRUE | TRUE | TRUE | FALSE | TRUE | FALSE | TRUE | Spell | |||||||||||||||||||||||||||||||||
19 | Yacs | Experiment Monitoring | none | None | None | none | none | 2018 | none | none | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | QuasiNoMod | Experiment, Run | TRUE | FALSE | FALSE | Open Source | Python | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | ||||||||||||||||||||||||||||||
20 | Atlas | Experiment Monitoring | Square/Dessa | none | none | 2016 | 11-50 | Toronto, CA | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | PyCallback | TRUE | FALSE | FALSE | Open Source | Python | FALSE | TRUE | TRUE | FALSE | TRUE | FALSE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | Dessa | ||||||||||||||||||||||||||||||||
21 | Sirio ML | End-to-End | SirioML | None | None | None | none | none | none | none | none | 0 | 0 | 0 | 0 | FALSE | none | TRUE | FALSE | FALSE | Open source | Python | TRUE | TRUE | TRUE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | ||||||||||||||||||||||||||||||||||
22 | DataRobot MLOps | MLOps | none | none | 2012 | 1,001-5,000 | Boston, Massachusetts | not published | not published | not published | not published | not published | not published | FALSE | FALSE | FALSE | Closed Source | DataRobot | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
23 | DBT | DataOps-Analytics | Fishtown Analytics | none | 2016 | 11-50 | Philedelphia USA | Apache 2.0 | free | $50/u/mo | none | not published | FALSE | none | TRUE | FALSE | FALSE | Open Source | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
24 | FlexP | data-flow programming framework | Seznam.cz a.s. | none | none | none | none | none | none | Czech Republic | BSD-3-Clause License | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open Source | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
25 | Hopsworks | Feature Store, End-to-End | Logical Clocks | none | none | 2016 | 11-50 | Stockholm, Sweden | AGPL V3.0 | free | not published | not published | not published | TRUE | 1 TB | FeatureGroup, Training Data, Experiments | FALSE | FALSE | FALSE | Open Source | Python, Scala, Java | TRUE | TRUE | TRUE | TRUE | TRUE | Hudi, Delta IO | FALSE | TRUE | Tensorboard | Prometheus/Grafana | FALSE | FALSE | TRUE | KFServing | FALSE | FALSE | FALSE | FALSE | Company Representative? | Hopsworks | |||||||||||||||||||||||||||||||||
26 | ML Pipeline | ML workflow organization | Ahmed Shariff | none | none | none | none | none | Winnipeg, Canada | MIT License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open Source | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
27 | CNVRG | end-to-end | Yochay Ettun | https://www.linkedin.com/company/cnvrg-io/ | none | https://cnvrg.io/ | none | none | 2016 | 11-50 | Jerusalem, Israel | freemium | not published | not published | not published | not published | not published | FALSE | FALSE | FALSE | Closed Source, Freemium | |||||||||||||||||||||||||||||||||||||||||||||||||||||
28 | Jovian ML | Courses | Jovian | https://www.linkedin.com/school/jovianai/ | https://jovian.ml/ | none | none | 2-10 | San Francisco, California | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open Source | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
29 | Datajoint | programming scientific databases and computational data pipelines. | Datajoint | none | none | none | none | none | LGPL-2.1 License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open Source | Python, Matlab | Datatron | |||||||||||||||||||||||||||||||||||||||||||||||||||||
30 | Studio | model management framework | studio.ml | none | none | none | none | none | Apache-2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open Source | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
31 | SACRED | Experiment Monitoring | IDSIA | None | none | none | none | none | None | Lugano Switzerland | MIT | 0 | 0 | 0 | 0 | FALSE | none | Experiment ID (Name and ID) | FALSE | FDecorators | Experiment | TRUE | FALSE | FALSE | Open Source | Python | TRUE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | |||||||||||||||||||||||||||||
32 | Datmo | Datmo | none | none | 2019 | none | Palo Alto, CA | MIT License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open Source | Python | Datmo | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
33 | Lore | Instacart | none | none | none | none | none | none | San Francisco, CA | MIT License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open Source | Python | |||||||||||||||||||||||||||||||||||||||||||||||||||||
34 | FORGE | Adam Kosiorek | none | none | none | none | none | none | Oxford, United Kingdom | GPL-3.0 License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open Source | Python, jupyter notebook | |||||||||||||||||||||||||||||||||||||||||||||||||||||
35 | Sumatra | none | none | none | none | none | none | none | BSD-2-Clause License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open source | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
36 | RandOpt | Séb Arnold | none | none | none | none | none | none | Los Angeles | Apache License 2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open source | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
37 | feature Forge | Machinalis | none | none | none | none | none | none | none | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open Source | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
38 | ModelChimp | ModelChimp | none | none | 2018 | 2-10 | Bangalore, Karnataka | BSD-2-Clause License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Open Source | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
39 | PolyAxon | polyaxon | none | none | none | 2-10 | Berlin, Berlin | Apache-2.0 License | free | $99/u/month billed annually, 125 billed monthly. | $149/u/month billed annually, 185 billed monthly. | not published | FALSE | none | FALSE | FALSE | FALSE | Closed Source | Polyaxon | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
40 | Optuna | Optuna | none | none | 2018 | none | none | MIT License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
41 | ML Commons/MLPerf | MLCommons | none | none | 2-10 | none | none | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | TRUE | non profit | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
42 | ML Commons/MLPerf-training | MLCommons | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
43 | ML Commons/MLPerf-inference | MLCommons | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
44 | ML Commons/MLPerf-inference_policies | MLCommons | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
45 | ML Commons/MLPerf-power | MLCommons | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
46 | ML Commons/MLPerf-logging | MLCommons | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
47 | ML Commons/MLPerf-mlcube | MLCommons | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
48 | ML Commons/MLPerf-tiny | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
49 | OctoML | OctoML | https://www.linkedin.com/company/octoml/ | none | none | none | none | 11-50 | Seattle, WA | none | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Closed Source | OctoML | |||||||||||||||||||||||||||||||||||||||||||||||||||||
50 | Algorithmia | All-in-one-Deployment | Algorithmia | 2014 | 50+ | Seattle,WA | FaaS | TRUE | FALSE | TRUE | Closed Source | Python, R, Scala, Java, Rust, Javascript, Ruby | TRUE | FALSE | TRUE | TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | TRUE | TRUE | TRUE | FALSE | FALSE | TRUE | TRUE | TRUE | TRUE | TRUE | Diego | Algorithmia | |||||||||||||||||||||||||||||||||||||||||
51 | Algorithmia-Competition | Competition | Algorithmia | None | none | none | 2014 | 50+ | Seattle,WA | MIT License | not published | not published | not published | not published | FALSE | none | TRUE | FALSE | TRUE | Open Source | Aaron Soellinger | |||||||||||||||||||||||||||||||||||||||||||||||||||||
52 | Microprediction | Microprediction | none | none | 2019 | 0-1 | West Palm Beach, US | none | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Aaron Soellinger | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
53 | Flyte | End-to-end containerized Orchestration of Data and ML Workflows | None | https://github.com/lyft/flyte | OSS(2019), founded(2017) | Seattle, WA | Apache 2.0 | CLI,UI,Python,Java,Golang,K8s,Containers | FaaS | TRUE | FALSE | FALSE | Open source | Go | Python, Java, agnostic | TRUE | TRUE | TRUE | TRUE | FALSE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | FALSE | Flyte Rep | Flyte | ||||||||||||||||||||||||||||||||||||||||||||
54 | MetaFlow | Netflix Open Source Platform | none | none | none | 11-50 | Ottawa, Ontario | Apache-2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Metaflow | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
55 | UbiOps | Serving, Orchestration & Dataflow management | UbiOps | https://www.linkedin.com/company/ubiops/ | https://github.com/ubiops | 2016 | 11-50 | The Hague, The Netherlands | UI, CLI, Python, Swagger | FALSE | FALSE | FALSE | Closed Source, Freemium + Enterprise | Python, R | TRUE | TRUE | TRUE | TRUE | TRUE | FALSE | TRUE | Anouk Dutree | ||||||||||||||||||||||||||||||||||||||||||||||||||||
56 | Dataiku | Dataiku | https://www.linkedin.com/company/dataiku/ | none | 2013 | 201-500 | Paris & NY | Apache-2.0 | 14-Day Free Trial | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Dataiku | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
57 | Dataspine | End-to-end- focus on serving, testing, monitoring | Dataspine | none | none | 2017 | 2-10 | Toronto, ON | none | 14-day free trial | Starting at $200/month | not published | not published | FALSE | none | TRUE | TRUE | TRUE | Coming Soon | Coming Soon | Coming Soon | TRUE | TRUE | TRUE | TRUE | |||||||||||||||||||||||||||||||||||||||||||||||||
58 | Allegro AI | ClearML | https://www.linkedin.com/company/clearml/ | 2016 | 11-50 | Tel-Aviv, IL | Apache-2.0 | Open Source | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Allegro AI/TRAINS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
59 | Iguazio | Nuclio | https://www.linkedin.com/company/iguazio/ | none | none | 2014 | 51-200 | Herzliya, Israel | Apache-2.0 | Open Source | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | TRUE | TRUE | Iguazio | |||||||||||||||||||||||||||||||||||||||||||||||||||||
60 | MLeap | End-to-End,DataOps-Analytics | Combust | none | none | none | none | none | California US | Apache-2.0 | 0 | 0 | 0 | 0 | FALSE | none | Spark/Sklearn | FALSE | FALSE | FALSE | Scala | |||||||||||||||||||||||||||||||||||||||||||||||||||||
61 | sisyphus | General workflow | Human Language Technology and Pattern Recognition Group | none | none | none | none | none | none | none | MPL-2.0 License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
62 | Soda | DataOps-? | soda | none | none | 2018 | 11-50 | Brussels, Brussels | Apache-2.0 | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | TRUE | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
63 | Abacus AI | All-in-one-AutoML | Abacus | https://www.linkedin.com/company/abacusai/ | none | none | none | 2019 | 11-50 | Bay Area | none | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Abacus AI | ||||||||||||||||||||||||||||||||||||||||||||||||||||
64 | Aible | All-in-one-Serving | Aible | https://www.linkedin.com/company/aible/ | none | none | none | 2018 | 11-50 | Bay Area | none | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Aible | ||||||||||||||||||||||||||||||||||||||||||||||||||||
65 | Cubonacci | All-in-one-AI Apps platform | Cubonacci | none | none | none | 2018 | 2-10 | Netherlands | none | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Cubonacci | |||||||||||||||||||||||||||||||||||||||||||||||||||||
66 | Databricks | All-in-one-Data management | Databricks | none | 2013 | 1001-5000 | Bay Area | 1;2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Databricks | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
67 | Datagrok | All-in-one-Data processing | Datagrok | none | none | 2019 | 2-10 | Pennsylvania | none | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Datagrok | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
68 | dotData | All-in-one-Feature engineering | dotData | none | none | none | 2018 | 11-50 | Bay Area | none | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | dotData | |||||||||||||||||||||||||||||||||||||||||||||||||||||
69 | Elementl | All-in-one-Workflow orchestration | none | 2018 | Bay Area | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Elementl | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
70 | Dagster | Elementl | none | none | none | none | Apache-2.0 | 0 | 0 | 0 | 0 | FALSE | none | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
71 | H2O | All-in-one-AI Apps platform | H2O | https://www.linkedin.com/company/h2oai/ | https://github.com/h2oai | none | 2012 | 201-500 | Bay Area | none | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | H2O | ||||||||||||||||||||||||||||||||||||||||||||||||||||
72 | HIVE | All-in-one-Labeling | HIVE | https://www.linkedin.com/company/hiveai/ | none | none | none | 2017 | 51-200 | Bay Area | none | not published | not published | not published | not published | FALSE | none | 1;2 | a lot of solutions for different industries therfore various pricing plans | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | HIVE | ||||||||||||||||||||||||||||||||||||||||||||||||||
73 | kedro | All-in-one-AI Apps platform | quantumblacklabs | none | none | none | none | none | none | none | Apache-2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | kedro | |||||||||||||||||||||||||||||||||||||||||||||||||||
74 | Michelangelo | All-in-one-Workflow orchestration | Querlo | https://www.linkedin.com/company/querlo/ | none | none | none | none | none | none | none | 0 | 0 | 0 | 0 | FALSE | none | still in beta | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Michelangelo | |||||||||||||||||||||||||||||||||||||||||||||||||||
75 | Obliviously AI | All-in-one-AI Apps platform | Obliviously AI | none | none | none | none | none | none | none | none | free | $75/u/mo | $180/mo | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Obliviously AI | ||||||||||||||||||||||||||||||||||||||||||||||||||||
76 | Peltarion | All-in-one-AI Apps platform | Peltarion | https://www.linkedin.com/company/peltarion/ | none | none | 2004 | 51-200 | Sweden | free | $49/u/mo | $499/mo | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Peltarion | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
77 | Petuum | All-in-one-Data management | Petuum, Inc. | https://www.linkedin.com/company/petuum/ | none | none | none | 2016 | 51-200 | Pennsylvania | not published | not published | not published | not published | FALSE | none | no pricing available | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Petuum | ||||||||||||||||||||||||||||||||||||||||||||||||||||
78 | Picsell.ia | All-in-one-Computer Vision | Picsell | https://www.linkedin.com/company/picsell-ia/ | none | none | none | 2019 | 2-10 | France | free | €50/3 users/mo | not published | €400/5+ users/mo | available only in premium and entreprise plans | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Picsell.ia | |||||||||||||||||||||||||||||||||||||||||||||||||||||
79 | Robust AI | All-in-one-Robotics | Robust AI | none | none | none | 2019 | 11-50 | Palo Alto, California | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Robust AI | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
80 | Snorkel AI | All-in-one-AI Apps platform | Snorkel AI | https://snorkel.ai | none | none | none | 11-50 | Bay Area | Apache-2.0 | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Snorkel AI | ||||||||||||||||||||||||||||||||||||||||||||||||||||
81 | Stradigi AI | All-in-one-AI apps platform | Stradigi AI | none | none | none | 2017 | 51-200 | Canada | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Stradigi AI | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
82 | Supervisely | All-in-one-Computer vision | Deep Systems | none | none | 2013 | 2-10 | San Francisco, CA | free | coming soon | coming soon | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Supervisely | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
83 | Tecton | All-in-one-Deployment | Tecton | https://www.linkedin.com/company/tectonai/ | none | https://tecton.ai/ | none | none | 2019 | 11-50 | San Francisco & Newyork | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Tecton | ||||||||||||||||||||||||||||||||||||||||||||||||||||
84 | Xpanse AI | All-in-one-AI Apps platform | Xpanse AI | none | none | none | 2015 | 2-10 | Dublin | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Xpanse AI | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
85 | Aircloak | Data pipeline-Privacy | Aircloak | none | none | none | 2012 | 2-10 | Germany | not published | not published | not published | not published | FALSE | none | Free for NGOs | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Aircloak | |||||||||||||||||||||||||||||||||||||||||||||||||||||
86 | Alluxio | Data pipeline-Data management | Alluxio | none | 2015 | 51-200 | San Mateo, California | Apache-2.0 | free | not published | not published | not published | FALSE | none | open source for individuals | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Alluxio | |||||||||||||||||||||||||||||||||||||||||||||||||||||
87 | Alteryx | Data pipeline-Data management | Alteryx | https://www.linkedin.com/company/alteryx/ | https://github.com/alteryx | none | none | 1997 | 1001-5000 | Irvine, CA | not published | not published | not published | not published | FALSE | none | 1;2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Alteryx | ||||||||||||||||||||||||||||||||||||||||||||||||||||
88 | Amundsen | Data pipeline-Database/Query | none | none | none | none | none | none | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Amundsen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
89 | Anodot | Data pipeline-Data monitoring | Anodot | https://www.linkedin.com/company/anodot/ | https://github.com/anodot | none | none | 2014 | 51-200 | California | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Anodot | |||||||||||||||||||||||||||||||||||||||||||||||||||||
90 | Apache Druid | Data pipeline-Database/Query | apache | none | none | none | none | none | apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Apache Druid | |||||||||||||||||||||||||||||||||||||||||||||||||||||
91 | Apache Hudi | Data pipeline-Data warehouse | Apache | none | none | none | none | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Apache Hudi | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
92 | Apache ORC | Data pipeline-File format | Apache | none | none | none | none | none | none | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Apache ORC | ||||||||||||||||||||||||||||||||||||||||||||||||||||
93 | Aparavi | Data pipeline-Data management | Aparavi | none | none | none | 2017 | 51-200 | California | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Aparavi | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
94 | AresDB | Data pipeline-Database/Query | Uber | none | none | none | 2019 | none | none | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | AresDB | ||||||||||||||||||||||||||||||||||||||||||||||||||||
95 | Ascend.io | Data pipeline-Data management | Ascend | none | none | none | 2015 | 11-50 | Palo Alto, California | (see issues column) | (see issues column) | (see issues column) | (see issues column) | FALSE | none | 1 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Ascend.io | |||||||||||||||||||||||||||||||||||||||||||||||||||||
96 | AtScale | Data pipeline-Data management | AtScale | none | none | none | 2013 | 51-200 | San Mateo, CALIFORNIA | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | AtScale | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
97 | Cazena | Data pipeline-Data management | Cazena | https://www.linkedin.com/company/cazena/ | none | none | none | 2015 | 51-200 | Waltham , MA | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Cazena | |||||||||||||||||||||||||||||||||||||||||||||||||||||
98 | ClearSky Data | Data pipeline-Storage | none | none | none | none | 2014 | 11-50 | Boston | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | ClearSky Data | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
99 | Cohesity | Data pipeline-Data management | Cohesity | https://www.linkedin.com/company/cohesity/ | none | none | none | 2013 | 1001-5000 | San Jose, California | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Cohesity | |||||||||||||||||||||||||||||||||||||||||||||||||||||
100 | Confluent | Data pipeline-Stream processing | Confluent | none | none | 2014 | 1001-5000 | Mountain View, California | free trial | not published | not published | not published | TRUE | 5TB | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Confluent | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
101 | cuDF | Data pipeline-Data processing | NVIDIA | none | https://github.com/rapidsai | https://rapids.ai/ | 2018 | Bay Area | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | cuDF | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
102 | Dask | Data pipeline-Data processing | none | none | https://github.com/dask | https://dask.org/ | none | 2015 | none | Remote | BSD-3-Clause License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Dask | |||||||||||||||||||||||||||||||||||||||||||||||||||
103 | Datatable | Data pipeline-Data processing | H2O | none | none | none | none | 2017 | none | Bay Area | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Datatable | ||||||||||||||||||||||||||||||||||||||||||||||||||||
104 | Dataturks | Data pipeline-Labeling | Walmart | none | none | none | none | 2018 | none | India | website not safe therfore no infos was retrieved | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Dataturks | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
105 | Datera | Data pipeline-Storage | Datera | https://www.linkedin.com/company/datera/ | https://datera.io/ | none | none | 2013 | 51-200 | Santa Clara, California | Apache 2.0 | free trial | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Datera | ||||||||||||||||||||||||||||||||||||||||||||||||||||
106 | DefinedCrowd | Data pipeline-Data generation | DefinedCrowd | none | none | none | 2015 | 201-500 | Seattle | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | DefinedCrowd | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
107 | Delta Lake | Data pipeline-Data warehouse | Databricks | none | https://delta.io/ | none | 2019 | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Delta Lake | |||||||||||||||||||||||||||||||||||||||||||||||||||||
108 | Doccano | Data pipeline-Labeling | doccano | none | none | none | 2018 | Tokyo | MIT License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Doccano | |||||||||||||||||||||||||||||||||||||||||||||||||||||
109 | Dolt | Data pipeline-Versioning | Dolt | none | 2018 | 2-10 | Santa Monica, CA | Apache 2.0 | free | 0 | 0 | 0 | TRUE | none | storage available in the pro plan | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Dolt | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
110 | Dremio | Data pipeline-Data management | Dremio | https://www.linkedin.com/company/dremio/ | https://github.com/dremio | none | 2015 | 51-200 | Santa Clara, California | free trial | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Dremio | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
111 | Elastifile | Data pipeline-Storage | none | none | none | none | 2013 | Bay Area | 1 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Elastifile | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
112 | erwin | Data pipeline-Data management | Parallax Capital Partners | https://www.linkedin.com/company/erwin/ | none | none | none | 2016 | 51-200 | Melville, NY | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | erwin | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
113 | Excelero | Data pipeline-Storage | none | https://www.linkedin.com/company/excelero/ | none | none | none | 2014 | 51-200 | San Jose, CA | not published | not published | not published | not published | FALSE | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Excelero | ||||||||||||||||||||||||||||||||||||||||||||||||||||
114 | Facets | Data pipeline-Visualization | none | none | none | 2017 | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Facets | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
115 | FEAST | Data pipeline-Feature engineering | FEAST | none | 2019 | Asia | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | FEAST | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
116 | Figure Eight | Data pipeline-Labeling | Appen | https://www.linkedin.com/company/appen/ | none | none | none | 1997 | 501-1000 | Bay Area | not published | not published | not published | not published | FALSE | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Figure Eight | ||||||||||||||||||||||||||||||||||||||||||||||||||||
117 | Fluree | Data pipeline-Database/Query | Fluree | https://github.com/fluree/ | https://flur.ee/ | 2017 | 2-10 | Winston Salem, NC | free | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Fluree | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
118 | Gemini Data | Data pipeline-Data management | Gemini Data | none | none | none | 2015 | 11-50 | Greenbrae, California | not published | not published | not published | not published | FALSE | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Gemini Data | |||||||||||||||||||||||||||||||||||||||||||||||||||||
119 | Git LFS | Data pipeline-Versioning | Atlassian, GitHub | none | none | none | 2014 | none | Remote | MIT License | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Git LFS | ||||||||||||||||||||||||||||||||||||||||||||||||||||
120 | Gluent | Data pipeline-Visualization | Gluent | none | none | none | 2015 | 11-50 | Texas | not published | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Gluent | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
121 | Graviti | Data pipeline-Data management | https://www.linkedin.com/company/graviti-ai/ | none | none | none | 2019 | China | free trial | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Graviti | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
122 | Gretel AI | Data pipeline-Privacy | Gretel AI | https://www.linkedin.com/company/gretelai/ | https://github.com/gretelai | https://gretel.ai/ | none | 2019 | 11-50 | San Diego | Apache 2.0 | free | not published | not published | not published | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Gretel AI | |||||||||||||||||||||||||||||||||||||||||||||||||||
123 | Hammerspace | Data pipeline-Database/Query | Hammerspace | none | none | none | 2018 | 11-50 | Los Altos, California | not published | not published | not published | not published | none | none | 1;2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Hammerspace | |||||||||||||||||||||||||||||||||||||||||||||||||||||
124 | Heartex Label Studio | Data pipeline-Labeling | Heartex Label Studio | https://www.linkedin.com/company/heartex/ | none | none | 2018 | 2-10 | San Francisco, California | trial | not published | not published | not published | FALSE | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Heartex Label Studio | |||||||||||||||||||||||||||||||||||||||||||||||||||||
125 | Igneous | Data pipeline-Data management | Rubrik | https://www.linkedin.com/company/igneous/ | none | none | none | 2013 | 51-200 | Seattle | not published | not published | not published | not published | FALSE | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Igneous | ||||||||||||||||||||||||||||||||||||||||||||||||||||
126 | iMerit | Data pipeline-Data management | Imply | https://www.linkedin.com/company/imply/ | https://imply.io/ | none | none | 2015 | 51-200 | Burlingame, California | not published | not published | not published | not published | FALSE | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Imply | ||||||||||||||||||||||||||||||||||||||||||||||||||||
127 | Incorta | Data pipeline-Data processing | Incorta | https://www.linkedin.com/company/incorta/ | none | none | none | 2013 | 201-500 | San Mateo, CA | not published | not published | not published | not published | FALSE | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Incorta | ||||||||||||||||||||||||||||||||||||||||||||||||||||
128 | Kimono Labs | Data pipeline-Data generation | Palantir | none | none | none | 2012 | 11-500 | Salt Lake City, Utah | 0 | 0 | 0 | 0 | FALSE | none | not really an ML related solution | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Kimono Labs | |||||||||||||||||||||||||||||||||||||||||||||||||||||
129 | Koalas | Data pipeline-Data processing | Databricks | none | none | none | 2019 | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | FALSE | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Koalas | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
130 | Komprise | Data pipeline-Storage | Komprise | none | none | none | 2014 | 51-200 | Campbell, CA | free trial | not published | not published | not published | FALSE | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Komprise | |||||||||||||||||||||||||||||||||||||||||||||||||||||
131 | Kyvos Insights | Data pipeline-Database/Query | Kyvos Insights | none | none | none | 2015 | 51-200 | Los Gatos, California | demo | demo | demo | demo | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Kyvos Insights | |||||||||||||||||||||||||||||||||||||||||||||||||||||
132 | Labelbox | Data pipeline-Labeling | Labelbox | https://www.linkedin.com/company/labelbox/ | none | none | none | 2017 | 11-50 | San Francisco, California | Free | not published | not published | not published | none | none | 5 users in the free plan | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Labelbox | ||||||||||||||||||||||||||||||||||||||||||||||||||||
133 | LabelImg | Data pipeline-Labeling | darrenl | none | none | none | none | 2016 | Canada | MIT License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | LabelImg | ||||||||||||||||||||||||||||||||||||||||||||||||||||
134 | Materialize | Data pipeline-Stream processing | Materialize | none | 2019 | 11-50 | NYC | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Materialize | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
135 | Milvus | Data pipeline-Database/Query | Zilliz | none | https://milvus.io/ | none | 2019 | China | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Milvus | |||||||||||||||||||||||||||||||||||||||||||||||||||||
136 | Modin | Data pipeline-Data processing | Modin | none | none | none | 2018 | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Modin | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
137 | Naveego | Data pipeline-Data processing | Naveego | none | none | none | 2014 | 11-50 | Traverse City, MI | free trial | not published | not published | not published | none | none | 1 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Naveego | |||||||||||||||||||||||||||||||||||||||||||||||||||||
138 | Octopai | Data pipeline-Data management | Octopai | https://www.linkedin.com/company/octopai/ | none | none | none | 2015 | 11-50 | Israel | demo | demo | demo | demo | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Octopai | |||||||||||||||||||||||||||||||||||||||||||||||||||||
139 | Parquet | Data pipeline-File format | Twitter, Cloudera | none | none | none | none | 2013 | Bay Area | 1 | it's a databricks product | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Parquet | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
140 | Pilosa | Data pipeline-Database/Query | Pilosa | https://www.linkedin.com/company/pilosa/ | https://github.com/pilosa | none | 2017 | 11-50 | Austin, Texas | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Pilosa | ||||||||||||||||||||||||||||||||||||||||||||||||||||
141 | Playment | Data pipeline-Labeling | Playment | none | none | none | 2015 | 51-200 | Bengaluru, Karnataka | demo | demo | demo | demo | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Playment | |||||||||||||||||||||||||||||||||||||||||||||||||||||
142 | Presto | Data pipeline-Database/Query | Presto | 2019 | 51-200 | San Francisco, California | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Presto | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
143 | Prodigy | Data pipeline-Labeling | Explosion AI | none | https://prodi.gy/ | none | none | 2017 | Germany | $390 | $490 | $490 | $490 | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Prodigy | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
144 | Prometheus | Data pipeline-Monitoring | Apache/PromLabs | none | none | none | 2012 | Germany | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Prometheus | |||||||||||||||||||||||||||||||||||||||||||||||||||||
145 | Qri | Data pipeline-Versioning | Qri | none | https://github.com/qri-io | https://qri.io/ | none | 2016 | NYC | GPL-3.0 License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Qri | ||||||||||||||||||||||||||||||||||||||||||||||||||||
146 | Quilt Data | Data pipeline-Versioning | Quilt | none | none | 2015 | Bay Area | Apache 2.0 | Free | $1000/mo | not published | not published | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Quilt Data | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
147 | Quobyte | Data pipeline-Storage | Quobyte | https://www.linkedin.com/company/quobyte/ | https://github.com/quobyte | none | none | 2013 | 11-50 | Santa Clara, California | Free | not published | TRUE | 150TB HDD + 30 TB SSD or 10TB usable on cloud | Cluster plan cost: $12,999/year with 24/7 support, plus tax $8,999/year with 9-5 support, plus tax | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Quobyte | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
148 | Rockset | Data pipeline-Database/Query | Rockset | none | none | 2016 | 11-50 | San Mateo, California | Free | $0.0458 / hour | $0.7989 / hour | not published | TRUE | 2GB | 1 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Rockset | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
149 | Rubrik | Data pipeline-Data management | Rubrik | none | none | 2014 | 1001-5000 | Palo Alto, CA | not published | not published | not published | not published | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Rubrik | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
150 | Scale AI | Data pipeline-Data generation | Scale | https://www.linkedin.com/company/scaleai/ | none | none | none | 2016 | 201-500 | San Francisco, California | 1;2 | cost depends on each product. | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Scale AI | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
151 | Scrapinghub | Data pipeline-Data generation | Scrapinghub | none | none | 2010 | 51-200 | Ballincollig, Cork | not published | not published | not published | not published | none | none | 2 | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Scrapinghub | |||||||||||||||||||||||||||||||||||||||||||||||||||||
152 | Segments.ai | Data pipeline-Labeling | Segments | none | none | 2020 | 2-10 | Leuven, Leuven | Free | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Segments.ai | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
153 | Sisu | Data pipeline-Analytics platform | Sisu | none | none | none | 2018 | 51-200 | San Francisco, California | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Sisu | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
154 | Snorkel | Data pipeline-Labeling | Snorkel AI | none | https://snorkel.ai | none | none | 2016 | 11-50 | Palo Alto, California | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Snorkel | ||||||||||||||||||||||||||||||||||||||||||||||||||||
155 | Spark | Data pipeline-Data processing | Apache | 2009 | 0 | 0 | 0 | 0 | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Spark | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
156 | SQLFlow | Data pipeline-Database/Query | Alibaba | none | none | none | 2019 | China | 0 | 0 | 0 | 0 | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | SQLFlow | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
157 | Starburst Data | Data pipeline-Database/Query | Starburst data | none | none | 2017 | 51-200 | Boston | Free | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Starburst Data | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
158 | Storbyte | Data pipeline-Storage | Storbyte | none | none | none | 2014 | 51-200 | DC | not published | not published | not published | not published | Not an ML product | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Storbyte | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
159 | Superb AI | Data pipeline-Data management | Superb AI | https://github.com/Superb-AI-Suite | none | none | 2018 | 11-50 | San Mateo , California | Free | Free trial | not published | not published | TRUE | Store Up to 20,000 Data Assets | 1 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Superb AI | |||||||||||||||||||||||||||||||||||||||||||||||||||||
160 | Synthetaic | Data pipeline-Data generation | Synthetaic | none | none | none | 2019 | 11-50 | Wisconsin | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Synthetaic | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
161 | Tamr | Data pipeline-Data management | Tamr | https://www.linkedin.com/company/tamrinc/ | none | none | none | 2012 | 51-200 | Boston | Demo | Demo | Demo | Demo | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Tamr | ||||||||||||||||||||||||||||||||||||||||||||||||||||
162 | TerminusDB | Data pipeline-Database/Query | TerminusDB | 2019 | 11-50 | Ireland | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Prolog | Python, JS | Yes | Yes | Yes | Soon | Yes | Tabsharani-2020/01/11 | TerminusDB | |||||||||||||||||||||||||||||||||||||||||||||||||
163 | Tumult Labs | Data pipeline-Privacy | Tumult Labs | none | none | none | none | 2019 | North Carolina | 0 | 0 | 0 | 0 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Tumult Labs | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
164 | V7Labs | Data pipeline-Labeling | V7Labs | https://www.linkedin.com/company/v7labs/ | none | none | none | 2018 | 11-50 | UK | $150/mo | $450/mo | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | V7Labs | |||||||||||||||||||||||||||||||||||||||||||||||||||||
165 | Vaex | Data pipeline-Data processing | Vaex | https://www.linkedin.com/company/vaexio/ | https://vaex.io/ | none | none | 2015 | 2-10 | Netherlands | 0 | 0 | 0 | 0 | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Vaex | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
166 | Vearch | Data pipeline-Database/Query | Vearch | 2019 | China | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Vearch | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
167 | Vexata | Data pipeline-Storage | StorCentric | https://www.linkedin.com/company/vexata/ | none | none | none | 2013 | 51-200 | San Jose, CA | not published | not published | not published | not published | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Vexata | ||||||||||||||||||||||||||||||||||||||||||||||||||||
168 | Voxel51 // Scoop | Data pipeline-Labeling | https://www.linkedin.com/company/voxel51/ | none | 2016 | 2-10 | Ann Arbor, MI | 0 | 0 | 0 | 0 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Voxel51 // Scoop | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
169 | Waterline Data | Data pipeline-Data management | Hitachi Vantara | none | none | none | 2013 | 5001-10000 | Santa Clara, California | not published | not published | not published | not published | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Waterline Data | |||||||||||||||||||||||||||||||||||||||||||||||||||||
170 | Yellowbrick Data | Data pipeline-Data warehouse | Yellowbrick Data | none | none | none | 2014 | 51-200 | Palo Alto, California | $10k/month | not published | not published | not published | TRUE | NO | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Yellowbrick Data | |||||||||||||||||||||||||||||||||||||||||||||||||||||
171 | Zilliz | Data pipeline-Database/Query | Zilliz | https://www.linkedin.com/company/zilliz/ | none | none | none | 2017 | 51-200 | China | not published | not published | not published | not published | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Zilliz | ||||||||||||||||||||||||||||||||||||||||||||||||||||
172 | Blaize | Hardware-Edge devices | Blaize | https://www.linkedin.com/company/blaize-ai/ | none | none | none | 2010 | 201-500 | El Dorado Hills, California | not published | not published | not published | not published | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Blaize | ||||||||||||||||||||||||||||||||||||||||||||||||||||
173 | Boulder AI | Hardware-Edge devices | Boulder AI | none | none | none | 2017 | 11-50 | Boulder, Colorado | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Boulder AI | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
174 | BrainChip | Hardware-Edge devices | BrainChip | none | none | none | 2013 | 11-50 | Aliso Viejo, California | not published | not published | not published | not published | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | BrainChip | |||||||||||||||||||||||||||||||||||||||||||||||||||||
175 | Cambricon | Hardware-Accelerator | Cambricon | none | none | none | 2016 | 1001-5000 | China | not published | not published | not published | not published | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Cambricon | |||||||||||||||||||||||||||||||||||||||||||||||||||||
176 | Cerebras | Hardware-Accelerator | Cerebras | none | none | none | 2016 | 51-200 | Los Altos, California | not published | not published | not published | not published | none | none | It's not an ML framework/software | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Cerebras | |||||||||||||||||||||||||||||||||||||||||||||||||||||
177 | EdgeQ | Hardware-Edge devices | Vinay Ravuri | none | none | none | none | 2018 | Bay Area | none | none | none | none | none | none | still in developement | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | EdgeQ | |||||||||||||||||||||||||||||||||||||||||||||||||||||
178 | Graphcore | Hardware-Accelerator | Graphcore | none | none | 2016 | 51-200 | Bristol, Bristol | none | none | none | none | none | none | 2 | it's a processor for AI | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Graphcore | |||||||||||||||||||||||||||||||||||||||||||||||||||||
179 | GreenWaves Technologies | Hardware-Edge devices | GreenWaves Technologies | none | none | none | 2014 | 11-50 | Grenoble , Isere | none | none | none | none | none | none | 2 | IOT product | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | GreenWaves Technologies | ||||||||||||||||||||||||||||||||||||||||||||||||||||
180 | Groq | Hardware-Accelerator | Groq | https://www.linkedin.com/company/groq/ | none | none | none | 2016 | 11-50 | Mountain View, CA | none | none | none | none | none | none | 2 | hardware | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Groq | |||||||||||||||||||||||||||||||||||||||||||||||||||
181 | Habana Labs | Hardware-Edge devices | Intel | none | none | none | 2016 | 51-200 | Tel Aviv, Center | none | none | none | none | none | none | 2 | hardware | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Habana Labs | ||||||||||||||||||||||||||||||||||||||||||||||||||||
182 | Hailo | Hardware-Edge devices | Hailo | https://www.linkedin.com/company/hailo-ai/ | none | https://hailo.ai/ | none | none | 2017 | 51-200 | Tel Aviv, Israel | none | none | none | none | none | none | 2 | AI processor | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Hailo | ||||||||||||||||||||||||||||||||||||||||||||||||||
183 | Kneron | Hardware-Edge devices | Kneron | https://www.linkedin.com/company/kneron/ | none | none | none | 2015 | 51-200 | San Diego, California | none | none | none | none | none | none | 2 | hardware | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Kneron | |||||||||||||||||||||||||||||||||||||||||||||||||||
184 | LeapMind | Hardware-Edge devices | LeapMind | none | none | none | 2012 | 51-200 | Tokyo | none | none | none | none | none | none | not clear whats the product about | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | LeapMind | |||||||||||||||||||||||||||||||||||||||||||||||||||||
185 | Lightelligence | Hardware-Accelerator | Lightelligence | none | none | none | 2017 | 11-50 | Boston | none | none | none | none | none | none | hardware | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Lightelligence | |||||||||||||||||||||||||||||||||||||||||||||||||||||
186 | Luminous Computing | Hardware-Accelerator | Luminous Computing | none | none | none | 2018 | 11-50 | Mountain View, California | none | none | none | none | none | none | hardware | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Luminous Computing | |||||||||||||||||||||||||||||||||||||||||||||||||||||
187 | Mythic | Hardware-Edge devices | Mythic | https://www.linkedin.com/company/mythic-ai/ | none | none | none | 2012 | 51-200 | Austin, TX | none | none | none | none | none | none | 2 | hardware | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Mythic | |||||||||||||||||||||||||||||||||||||||||||||||||||
188 | Nuvia | Hardware-Accelerator | Nuvia | none | none | none | 2019 | 51-200 | Santa Clara , CA | none | none | none | none | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Nuvia | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
189 | Prophesee | Hardware-Edge devices | Prophesee | none | none | none | none | 2014 | none | France | none | none | none | none | none | none | 2 | not MLops | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Prophesee | |||||||||||||||||||||||||||||||||||||||||||||||||||
190 | SambaNova | Hardware-Accelerator | SambaNova | none | none | none | 2017 | 51-200 | Palo Alto, CA | none | none | none | none | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | SambaNova | |||||||||||||||||||||||||||||||||||||||||||||||||||||
191 | SiMa.ai | Hardware-Edge devices | SiMa.ai | https://www.linkedin.com/company/sima-ai/ | none | https://sima.ai/ | none | none | 2018 | 51-200 | San Jose, California | none | none | none | none | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | SiMa.ai | |||||||||||||||||||||||||||||||||||||||||||||||||||
192 | Syntiant | Hardware-Edge devices | Syntiant | none | none | none | 2017 | 51-200 | Irvine, California | none | none | none | none | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Syntiant | |||||||||||||||||||||||||||||||||||||||||||||||||||||
193 | Wave Computing | Hardware-Accelerator | Wave Computing | none | none | none | 2010 | 51-200 | Campbell, California | none | none | none | none | none | none | 2 | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Wave Computing | |||||||||||||||||||||||||||||||||||||||||||||||||||||
194 | Zero ASIC | Hardware-Edge devices | none | none | none | none | 2020 | none | Boston | none | none | none | none | none | none | Not clear | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Zero ASIC | |||||||||||||||||||||||||||||||||||||||||||||||||||||
195 | Airflow | Infrastructure-Workflow orchestration | Airbnb | none | 2015 | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Airflow | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
196 | Anyscale | Infrastructure-Cloud management | Anyscale | none | none | 2019 | 11-50 | Berkeley, CA | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Anyscale | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
197 | Backend AI | Infrastructure-Workflow orchestration | none | https://github.com/lablup | none | 2016 | Seoul | free | none | none | not published | none | none | 2 | pricing depends on on-premise or cloud | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Backend AI | |||||||||||||||||||||||||||||||||||||||||||||||||||||
198 | Cadence | Infrastructure-Workflow orchestration | Uber | none | none | none | 2017 | Bay Area | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Cadence | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
199 | Cloudera | Infrastructure-Cloud management | Cloudera | https://www.linkedin.com/company/cloudera/ | none | none | 2008 | 1001-5000 | Palo Alto, California | see notes | see notes | see notes | see notes | none | none | 1;2 | many products | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Cloudera | ||||||||||||||||||||||||||||||||||||||||||||||||||||
200 | Datadog | Infrastructure-Cloud management | Datadog | https://www.linkedin.com/company/datadog/ | none | none | none | 2010 | 1001-5000 | NYC | see notes | see notes | see notes | see notes | none | none | 1;2 | many products | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Datadog | |||||||||||||||||||||||||||||||||||||||||||||||||||
201 | Domino Data Lab | Infrastructure-Cloud management | Domino Data Lab | none | none | none | 2013 | 51-200 | San Francisco, CA | see notes | see notes | see notes | see notes | none | none | 2 | many products | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Domino Data Lab | ||||||||||||||||||||||||||||||||||||||||||||||||||||
202 | FloydHub | Infrastructure-Cloud management | FloydHub | none | none | 2016 | 2-10 | San Francisco, CA | Beginner: Free | Data Scientist: $9/mo | $99/mo | none | not published | TRUE | 10GB | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | FloydHub | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
203 | HYCU | Infrastructure-Cloud management | HYCU | https://www.linkedin.com/company/hycu/ | none | none | none | 2018 | 201-500 | Boston | see notes | see notes | see notes | see notes | none | none | 2 | many products | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | HYCU | |||||||||||||||||||||||||||||||||||||||||||||||||||
204 | Luigi | Infrastructure-Workflow orchestration | Spotify | 2012 | Sweden | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Luigi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
205 | Paperspace | Infrastructure-Cloud management | none | none | 2014 | 11-50 | NYC | see notes | see notes | see notes | see notes | TRUE | 10GB | 1;2 | many products and pricing plans | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Paperspace | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
206 | Prefect | Infrastructure-Workflow orchestration | Perfect | none | none | none | 2018 | none | DC | Free | $100/mo | none | not published | none | none | open source and closed source products | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Prefect | ||||||||||||||||||||||||||||||||||||||||||||||||||||
207 | Accord | Modeling & Training-Framework | César Roberto de Souza | https://www.linkedin.com/in/cesarrsouza/ | none | none | 2012 | France | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Accord | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
208 | AIMET | Modeling & Training-Model compression | Qualcomm | none | none | none | none | 2020 | none | San Diego | 0 | 0 | 0 | none | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | AIMET | ||||||||||||||||||||||||||||||||||||||||||||||||||||
209 | Alectio | Modeling & Training-Active learning | Alectio | https://www.linkedin.com/company/alectio/ | none | none | none | 2019 | 11-50 | Mountain View, California | free trial | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Alectio | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
210 | Alink | Modeling & Training-Framework | Alibaba | none | none | none | 2018 | China | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Alink | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
211 | AllenNLP | Modeling & Training-NLP | Incubator | none | none | 2016 | none | Seattle | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | AllenNLP | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
212 | Angel ML | Modeling & Training-Distributed | Tencent/Peking University | none | none | none | 2017 | Shenzhen | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Angel ML | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
213 | Apache Mahout | Modeling & Training-Framework | Apache | none | none | none | 2008 | Remote | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Apache Mahout | |||||||||||||||||||||||||||||||||||||||||||||||||||||
214 | Apache MXNet | Modeling & Training-Framework | DMLC | none | none | 2015 | Apache 2.0 | 0 | 0 | 0 | 0 | none | None | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Apache MXNet | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
215 | BentoML | Modeling & Training-Pretrained models | Christoph Molnar | none | none | none | none | 2018 | Bay Area | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | BentoML | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
216 | Boruta | Modeling & Training-Feature engineering | scikit-learn | none | none | none | 2010 | BSD-3-Clause License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Boruta | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
217 | Caffe | Modeling & Training-Framework | Berkeley | none | 2013 | Bay Area | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Caffe | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
218 | Catalyst | Modeling & Training-Framework | Catalyst | none | none | 2018 | Russia | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Catalyst | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
219 | Chainer | Modeling & Training-Framework | Preferred Networks | none | none | 2015 | Tokyo | MIT | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Chainer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
220 | CleverHans | Modeling & Training-Adversarial robustness | 2017 | Bay Area | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | CleverHans | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
221 | Colab | Modeling & Training-Notebook | 2017 | Bay Area | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Colab | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
222 | DAGsHub | Modeling & Training-Versioning | DAGsHub | https://www.linkedin.com/company/dagshub/ | none | none | 2019 | Israel | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | DAGsHub | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
223 | DarwinAI | Modeling & Training-Explanability | DarwinAI | https://www.linkedin.com/company/darwinai/ | none | none | none | 2017 | 11-50 | Waterloo, Ontario | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | DarwinAI | |||||||||||||||||||||||||||||||||||||||||||||||||||||
224 | DAWNBench | Modeling & Training-Benchmarking | Stanford | none | none | none | none | 2018 | Bay Area | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | DAWNBench | |||||||||||||||||||||||||||||||||||||||||||||||||||||
225 | DeepNote | Modeling & Training-Notebook | DeepNote | none | none | 2019 | 11-50 | Prague, Prague | Free | $12/u/mo | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | DeepNote | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
226 | Determined AI | Modeling & Training-AutoML | Determined AI | none | 2016 | 11-50 | San Francisco, California | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Determined AI | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
227 | Dialogflow | Modeling & Training-NLU | none | none | none | none | 2014 | Bay Area | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Dialogflow | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
228 | Dockship | Modeling & Training-Pretrained models | dockship | none | none | none | 2019 | 11-50 | India | 0 | 0 | 0 | none | none | platform for competitions and hiring data scientist/AI specialist | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Dockship | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
229 | euler | Modeling & Training-Distributed | Alibaba | none | none | none | none | 2018 | China | very poor website. no info shown. | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | euler | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
230 | explainX.ai | Modeling & Training-Interpretability | Pre-seed | none | 2020 | 11-50 | NYC | MIT | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | explainX.ai | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
231 | fastText | Modeling & Training-NLP | none | none | 2016 | Bay Area | MIT | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | fastText | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
232 | Featuretools | Modeling & Training-Feature engineering | Alteryx | none | 2018 | LA | BSD-3-Clause License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Featuretools | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
233 | FedAI (FATE) | Modeling & Training-Framework | Webank | none | none | 2019 | Shenzhen | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | FedAI (FATE) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
234 | Fiddler Labs | Modeling & Training-Interpretability | Fiddler | none | none | 2018 | 11-50 | Bay Area | MIT | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Fiddler Labs | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
235 | flair | Modeling & Training-NLP | Zalando | none | none | none | none | 2018 | Germany | none | none | none | none | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | flair | |||||||||||||||||||||||||||||||||||||||||||||||||||||
236 | Gensim | Modeling & Training-Framework | RaRe Technologies | none | none | 2012 | Czech | LGPL-2.1 License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Gensim | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
237 | GluonCV | Modeling & Training-Pretrained models | Microsoft | none | none | none | 2018 | Seattle | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | GluonCV | |||||||||||||||||||||||||||||||||||||||||||||||||||||
238 | Horovod | Modeling & Training-Distributed | Uber | none | none | none | 2017 | Bay Area | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Horovod | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
239 | Hugging Face | Modeling & Training-NLP | Hugging Face | none | 2016 | 11-50 | NYC | Free || become a supporter for $9/mo | $199/mo | $599/mo | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Hugging Face | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
240 | HyperOpt | Modeling & Training-Hyperparameter tuning | U. Waterloo | none | none | none | 2013 | Canada | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | HyperOpt | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
241 | InterpretML | Modeling & Training-Interpretability | Microsoft | none | none | none | 2019 | none | Seattle | MIT | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | InterpretML | ||||||||||||||||||||||||||||||||||||||||||||||||||||
242 | JAX | Modeling & Training-Framework | none | none | none | none | 2018 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | JAX | ||||||||||||||||||||||||||||||||||||||||||||||||||||
243 | Katib | Modeling & Training-Hyperparameter tuning | Kubeflow | none | none | none | none | 2018 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Katib | |||||||||||||||||||||||||||||||||||||||||||||||||||
244 | Kyndi | Modeling & Training-Interpretability | none | https://www.linkedin.com/company/kyndi/ | none | www.kyndi.com | none | none | 2019 | none | Bay Area | none | 2 | many products | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Kyndi | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
245 | LightGBM | Modeling & Training-Framework | Microsoft | none | none | none | 2016 | none | Seattle | MIT | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | LightGBM | ||||||||||||||||||||||||||||||||||||||||||||||||||||
246 | LIME | Modeling & Training-Interpretability | University of Washington | none | none | none | none | 2016 | none | Seattle | BSD-2-Clause License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | LIME | |||||||||||||||||||||||||||||||||||||||||||||||||||
247 | Lucid | Modeling & Training-Interpretability | none | https://lucid.ai/ | none | none | 2008 | 2-10 | Austin, TX | none | not published | not published | not published | not published | none | none | not clear | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Lucid | |||||||||||||||||||||||||||||||||||||||||||||||||||
248 | Ludwig | Modeling & Training-Framework | uber | none | http://ludwig.ai | none | none | 2019 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Ludwig | |||||||||||||||||||||||||||||||||||||||||||||||||||
249 | Matroid | Modeling & Training-Computer vision | Matroid | https://www.linkedin.com/company/matroid/ | https://github.com/matroid | none | none | 2016 | 11-50 | Palo Alto, California | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Matroid | |||||||||||||||||||||||||||||||||||||||||||||||||||||
250 | Mindspore | Modeling & Training-Framework | Huawei | none | none | none | none | 2020 | none | Shenzhen | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Mindspore | |||||||||||||||||||||||||||||||||||||||||||||||||||
251 | ML.NET | Modeling & Training-Framework | Microsoft | none | https://github.com/dotnet | none | 2018 | none | Seattle | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | ML.NET | |||||||||||||||||||||||||||||||||||||||||||||||||||||
252 | MLlib | Modeling & Training-Framework | Spark | none | none | none | none | 2010 | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | MLlib | |||||||||||||||||||||||||||||||||||||||||||||||||||||
253 | MLPerf | Modeling & Training-Benchmarking | MLcommons | none | none | none | none | 2018 | none | Bay Area | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | MLPerf | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
254 | NeMo | Modeling & Training-NLU | NVIDIA | none | none | none | none | 2019 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | NeMo | |||||||||||||||||||||||||||||||||||||||||||||||||||
255 | Netron | Modeling & Training-Visualization | Lutz Roeder | none | none | none | 2011 | none | Seattle | MIT | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Netron | ||||||||||||||||||||||||||||||||||||||||||||||||||||
256 | nteract | Modeling & Training-Notebook | Nteract | none | none | 2015 | none | Texas | BSD-3-Clause License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | nteract | |||||||||||||||||||||||||||||||||||||||||||||||||||||
257 | OpenSeq2Seq | Modeling & Training-NLP | NVIDIA | none | none | none | none | 2017 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | OpenSeq2Seq | |||||||||||||||||||||||||||||||||||||||||||||||||||
258 | Paddle | Modeling & Training-Distributed | Baidu | none | none | none | 2016 | none | China | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Paddle | ||||||||||||||||||||||||||||||||||||||||||||||||||||
259 | papermill | Modeling & Training-Notebook | Nteract | none | none | none | 2017 | none | Texas | BSD-3-Clause License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | papermill | ||||||||||||||||||||||||||||||||||||||||||||||||||||
260 | PerceptiLabs | Modeling & Training-Visual modeling | none | none | none | none | 2019 | Bay Area | still in developement | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | PerceptiLabs | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
261 | PlaidML | Modeling & Training-Hardware compatiblity | Intel | none | none | none | none | 2017 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | PlaidML | |||||||||||||||||||||||||||||||||||||||||||||||||||
262 | Pyro | Modeling & Training-Programming language | Uber | none | https://pyro.ai/ | none | none | 2017 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Pyro | |||||||||||||||||||||||||||||||||||||||||||||||||||
263 | PySyft | Modeling & Training-Privacy | OpenMined | none | none | none | none | 2017 | none | UK | MIT | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | PySyft | |||||||||||||||||||||||||||||||||||||||||||||||||||
264 | Pythia | Modeling & Training-Framework | none | none | none | 2018 | none | Bay Area | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Pythia | |||||||||||||||||||||||||||||||||||||||||||||||||||||
265 | PyTorch | Modeling & Training-Framework | none | 2015 | none | Bay Area | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | PyTorch | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
266 | PyTorch Lightning | Modeling & Training-Framework | Grid AI | none | 2019 | 11-50 | NYC | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | PyTorch Lightning | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
267 | Rasa | Modeling & Training-NLU | Rasa | https://www.linkedin.com/company/rasa./ | none | 2016 | 11-50 | Germany | Apache 2.0 | 0 | 0 | 0 | not published | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Rasa | |||||||||||||||||||||||||||||||||||||||||||||||||||||
268 | Ray | Modeling & Training-Distributed | UC Berkeley | none | https://ray.io/ | 2016 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Ray | |||||||||||||||||||||||||||||||||||||||||||||||||||||
269 | Replicate | Modeling & Training-Versioning | replicate | none | none | none | 2020 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Replicate | ||||||||||||||||||||||||||||||||||||||||||||||||||||
270 | River | Modeling & Training-Online learning | OnlineML | none | none | none | 2017 | France | BSD-3-Clause License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | River | |||||||||||||||||||||||||||||||||||||||||||||||||||||
271 | scikit-learn | Modeling & Training-Framework | scikit-learn | none | none | none | 2010 | none | Remote | BSD-3-Clause License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | scikit-learn | ||||||||||||||||||||||||||||||||||||||||||||||||||||
272 | scribble Data | Modeling & Training-Feature engineering | scribble Data | none | none | none | 2016 | 11-50 | India/Canada | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Python | Tabsharani-2020/01/11 | scribble Data | ||||||||||||||||||||||||||||||||||||||||||||||||||||
273 | SHAP | Modeling & Training-Interpretability | Scott Lundberg | none | none | none | none | 2017 | none | Seattle | MIT | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | SHAP | |||||||||||||||||||||||||||||||||||||||||||||||||||
274 | SigOpt | Modeling & Training-Hyperparameter tuning | Intel | https://www.linkedin.com/company/sigopt/ | https://github.com/sigopt | none | none | 2014 | none | Bay Area | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | SigOpt | |||||||||||||||||||||||||||||||||||||||||||||||||||||
275 | spaCy | Modeling & Training-NLP | Explosion AI | none | https://spacy.io/ | none | none | 2014 | none | Germany | MIT | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | spaCy | |||||||||||||||||||||||||||||||||||||||||||||||||||
276 | Streamlit | Modeling & Training-App interface | Streamlit | https://www.linkedin.com/company/streamlit/ | none | 2018 | 11-50 | Bay Area | Apache 2.0 | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Streamlit | |||||||||||||||||||||||||||||||||||||||||||||||||||||
277 | talos | Modeling & Training-Hyperparameter tuning | Autonomio | none | none | none | 2018 | none | Finland | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | talos | |||||||||||||||||||||||||||||||||||||||||||||||||||||
278 | Tazi.ai | Modeling & Training-AutoML | Tazi | none | none | none | 2015 | 11-50 | Turkey | not published | not published | not published | not published | none | none | Many products | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Tazi.ai | |||||||||||||||||||||||||||||||||||||||||||||||||||||
279 | Tensorboard | Modeling & Training-Experiment tracking | none | 2015 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Tensorboard | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
280 | TensorFlow | Modeling & Training-Framework | 2015 | Bay Area | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | TensorFlow | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
281 | Theano | Modeling & Training-Framework | MILA | none | none | none | none | 2008 | none | Canada | none | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Theano | |||||||||||||||||||||||||||||||||||||||||||||||||||
282 | TPOT | Modeling & Training-AutoML | UPen | none | none | none | none | 2016 | none | Pennsylvania | LGPL-3.0 License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | TPOT | |||||||||||||||||||||||||||||||||||||||||||||||||||
283 | TransmogrifAI | Modeling & Training-AutoML | Salesforce | none | none | none | none | 2017 | none | Bay Area | BSD-3-Clause License | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | TransmogrifAI | |||||||||||||||||||||||||||||||||||||||||||||||||||
284 | Truera | Modeling & Training-Explanability | Truera | https://www.linkedin.com/company/truera/ | none | none | none | 2019 | 11-50 | Redwood City, CA | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Truera | ||||||||||||||||||||||||||||||||||||||||||||||||||||
285 | tsfresh | Modeling & Training-Feature engineering | Blue Yonder | https://github.com/salesforce/TransmogrifAI | none | none | none | 1985 | 5001-10000 | Germany | not published | not published | not published | not published | none | none | Many products | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | tsfresh | |||||||||||||||||||||||||||||||||||||||||||||||||||
286 | Tune | Modeling & Training-Hyperparameter tuning | Ray | none | none | 2017 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Tune | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
287 | Turi Create | Modeling & Training-Framework | Apple | none | none | none | none | 2018 | Bay Area | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Turi Create | |||||||||||||||||||||||||||||||||||||||||||||||||||||
288 | Vowpal Wabbit | Modeling & Training-Online learning | Microsoft | none | none | none | 2010 | none | none | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Vowpal Wabbit | |||||||||||||||||||||||||||||||||||||||||||||||||||||
289 | XGBoost | Modeling & Training-Framework | DMLC | none | none | none | 2014 | none | none | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | XGBoost | ||||||||||||||||||||||||||||||||||||||||||||||||||||
290 | Argo | Serving-CI/CD | Intuit | none | none | 2018 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Argo | |||||||||||||||||||||||||||||||||||||||||||||||||||||
291 | Arize AI | Serving-Monitoring | Arize AI | https://www.linkedin.com/company/arizeai/ | none | none | none | 2019 | 11-50 | Berkeley, CA | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Arize AI | ||||||||||||||||||||||||||||||||||||||||||||||||||||
292 | Arthur AI | Serving-Monitoring | Arthur AI | https://www.linkedin.com/company/arthurai/ | none | none | none | 2018 | 11-50 | NYC | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Arthur AI | ||||||||||||||||||||||||||||||||||||||||||||||||||||
293 | Clipper | Serving-Web | Berkeley | none | http://clipper.ai/ | none | none | 2017 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Clipper | |||||||||||||||||||||||||||||||||||||||||||||||||||
294 | Core ML | Serving-Mobile | Apple | none | none | none | none | 2017 | Bay Area | none | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Core ML | |||||||||||||||||||||||||||||||||||||||||||||||||||||
295 | Cortex | Serving-Web | Cortex | none | none | 2019 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Cortex | |||||||||||||||||||||||||||||||||||||||||||||||||||||
296 | Dash | Serving-App interface | Plotly | none | none | 2015 | none | Canada | MIT | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Dash | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
297 | Deeplite | Serving-Model compression | Deeplite | https://www.linkedin.com/company/deeplite/ | none | none | none | 2018 | 11-50 | Canada | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Deeplite | ||||||||||||||||||||||||||||||||||||||||||||||||||||
298 | Evidently AI | Serving-Monitoring | Evidently AI | none | none | 2020 | 2-10 | Russia | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Evidently AI | |||||||||||||||||||||||||||||||||||||||||||||||||||||
299 | Formant | Serving-Robotics | Formant | none | none | none | 2017 | 11-50 | San Francisco, CA | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Formant | |||||||||||||||||||||||||||||||||||||||||||||||||||||
300 | Fritz AI | Serving-Mobile | Fritz AI | none | none | none | 2017 | 2-10 | Boston | none | see notes | see notes | see notes | see notes | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Fritz AI | |||||||||||||||||||||||||||||||||||||||||||||||||||||
301 | Gradio | Serving-App interface | none | none | none | 2018 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Gradio | |||||||||||||||||||||||||||||||||||||||||||||||||||||
302 | Inferrd | Serving-Deployment | inferrd | none | none | none | none | 2020 | none | NYC | none | Test it out (Free) | Shared CPU ($15/mo) | Dedicated CPU ($30/mo) | Shared GPU ($50/mo) | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Inferrd | ||||||||||||||||||||||||||||||||||||||||||||||||||||
303 | Losswise | Serving-Monitoring | Mathpix | none | none | none | 2017 | none | Bay Area | none | Free | $39/mo | none | none | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Losswise | |||||||||||||||||||||||||||||||||||||||||||||||||||||
304 | ML Kit | Serving-Mobile | none | none | none | none | 2018 | none | Bay Area | none | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | ML Kit | ||||||||||||||||||||||||||||||||||||||||||||||||||||
305 | MMdnn | Serving-Compatibility | Microsoft | none | none | 2017 | none | Seattle | MIT | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | MMdnn | |||||||||||||||||||||||||||||||||||||||||||||||||||||
306 | MNN | Serving-Inference | Alibaba | none | none | none | 2019 | none | China | none | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | MNN | ||||||||||||||||||||||||||||||||||||||||||||||||||||
307 | Mona Labs | Serving-Monitoring | Mona Labs | none | none | none | 2018 | none | Atlanta | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Mona Labs | |||||||||||||||||||||||||||||||||||||||||||||||||||||
308 | ncnn | Serving-Mobile | Tencent | none | none | none | 2017 | none | Shenzhen | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | ncnn | |||||||||||||||||||||||||||||||||||||||||||||||||||||
309 | Neural Network Distiller | Serving-Model compression | Intel | none | none | none | 2018 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Neural Network Distiller | ||||||||||||||||||||||||||||||||||||||||||||||||||||
310 | ONNX | Serving-Compatibility | ONNX | none | https://onnx.ai/ | none | 2018 | none | none | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | ONNX | ||||||||||||||||||||||||||||||||||||||||||||||||||||
311 | Plotly | Serving-App interface | none | https://www.linkedin.com/company/plotly/ | none | 2013 | none | Canada | MIT | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Plotly | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
312 | PredictionIO | Serving-Web | Salesforce | none | none | none | 2013 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | PredictionIO | ||||||||||||||||||||||||||||||||||||||||||||||||||||
313 | RelicX | Serving-CI/CD | RelicX | none | none | https://relicx.ai | none | none | 2020 | none | Bay Area | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | RelicX | |||||||||||||||||||||||||||||||||||||||||||||||||||
314 | superwise.ai | Serving-Monitoring | superwise.ai | none | none | none | 2019 | 11-50 | Israel | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Y | Tabsharani-2020/01/11 | superwise.ai | ||||||||||||||||||||||||||||||||||||||||||||||||||||
315 | TensorFlow Extended | Serving-Deployment | none | none | none | 2019 | none | Bay Area | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | TensorFlow Extended | |||||||||||||||||||||||||||||||||||||||||||||||||||||
316 | TensorFlow Lite | Serving-Mobile | none | none | 2019 | none | Bay Area | none | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | TensorFlow Lite | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
317 | TensorRT | Serving-Inference | NVIDIA | none | none | none | none | 2019 | none | Bay Area | none | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | TensorRT | |||||||||||||||||||||||||||||||||||||||||||||||||||
318 | Unravel Data | Serving-Monitoring | Unravel Data | none | none | none | 2013 | none | Bay Area | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Unravel Data | |||||||||||||||||||||||||||||||||||||||||||||||||||||
319 | Xnor.ai | Serving-Model compression | Apple | none | none | none | none | 2016 | none | Seattle | none | Website is broken | FALSE | FALSE | FALSE | Tabsharani-2020/01/11 | Xnor.ai | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
320 | Arrikto | Arrikyo | https://www.linkedin.com/company/arrikto/ | none | none | none | 2015 | 11-50 | San Mateo, California | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | TODO | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
321 | Kale | Kubeflow Kale | none | none | none | none | none | none | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | TODO | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
322 | Olive AI | End-to-End-HealthcareVertical | Olive AI | https://www.linkedin.com/company/oliveai/ | none | none | none | 2012 | 201-500 | Columbus OH | none | not published | not published | not published | not published | none | none | FALSE | FALSE | FALSE | TODO | |||||||||||||||||||||||||||||||||||||||||||||||||||||
323 | ZenML | maiot GmbH | https://www.linkedin.com/company/zenml/ | https://zenml.io/ | none | none | 2021 | 2-10 | none | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | TODO | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
324 | MLRun | MLRun | none | none | none | none | none | none | 0 | 0 | 0 | 0 | none | none | TODO | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
325 | Databand | Databand | none | none | none | none | none | Apache 2.0 | Open Source | not published | not published | not published | none | none | TODO | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
326 | Nuclio | Nuclio | none | https://nuclio.io/ | none | none | none | none | Apache 2.0 | Free | not published | not published | not published | none | none | TODO | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
327 | Apache Flink | Serving-Stream processing | Apache | none | none | none | 2011 | none | Germany | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Apache Flink | ||||||||||||||||||||||||||||||||||||||||||||||||||||
328 | Apache TVM | Serving-Inference | Apache | none | none | 2017 | none | none | Apache 2.0 | 0 | 0 | 0 | 0 | none | none | FALSE | FALSE | FALSE | OSS | Tabsharani-2020/01/11 | Apache TVM | |||||||||||||||||||||||||||||||||||||||||||||||||||||
329 | Kaskada | Kaskada | none | Kaskada.com | none | none | 2016 | 11-50 | Seattle, Washington | none | not published | not published | not published | not published | none | none | TODO | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
330 | Zetane Engine | Zetane Systems | https://www.linkedin.com/company/zetane/ | none | none | none | 2016 | 2-10 | Montreal, QC | none | see notes | see notes | see notes | see notes | none | none | Many pricing plans | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TRUE | TODO | ||||||||||||||||||||||||||||||||||||||||||||||||
331 | Aquarium | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
332 | K3ai |
A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | |
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1 | Cat | COUNTA of Name | SUM of $$$ (M) | ||||||||||||||||
2 | All-in-one | 27 | $2,553.80 | ||||||||||||||||
3 | Data pipeline | 90 | $3,805.55 | ||||||||||||||||
4 | Hardware | 22 | $2,752.70 | ||||||||||||||||
5 | Infrastructure | 15 | $1,375.00 | ||||||||||||||||
6 | Modeling & Training | 91 | $301.22 | ||||||||||||||||
7 | Serving | 39 | $223.60 | ||||||||||||||||
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A | B | C | D | E | F | G | H | I | J | K | L | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Name | Cat | SubCat | Series | $$$ (M) | Started | HQ | OSS | Change in 2020 | Website | Description | IF ACQ |
2 | Graphcore | Hardware | Accelerator | E | 682 | 2016 | UK | Raised 372M | https://www.graphcore.ai/ | With the world’s most advanced Event-Based Vision systems, inspired by human vision and built on the foundation of neuromorphic engineering. PROPHESEE is the revolutionary system that gives Metavision to machines, revealing what was previously invisible to them. | ||
3 | SambaNova | Hardware | Accelerator | C | 465.3 | 2017 | Bay Area | Raised 250M | https://sambanova.ai/ | AI insights, faster Cerebras is a computer systems company dedicated to accelerating deep learning. The pioneering Wafer-Scale Engine (WSE) – the largest chip ever built – is at the heart of our deep learning system, the Cerebras CS-1. | ||
4 | Nuvia | Hardware | Accelerator | B | 293 | 2019 | Bay Area | Raised 240M | https://nuviainc.com/ | SambaNova Systems is a computing startup focused on building machine learning and big data analytics platforms. | ||
5 | Wave Computing | Hardware | Accelerator | E | 203.3 | 2008 | Bay Area | https://wavecomp.ai/ | Human insight and decision making on a visual sensor. | |||
6 | Cambricon | Hardware | Accelerator | B | 200 | 2016 | China | http://www.cambricon.com/ | Always-On Voice powered by custom AI Silicon | |||
7 | Cerebras | Hardware | Accelerator | C | 112 | 2016 | Bay Area | https://www.cerebras.net/ | Intelligence at the edge of everywhere. Blaize unleashes the potential of AI to drive leaps in the value that technology delivers to transform markets and improve the way we all work and live. | |||
8 | Hailo | Hardware | Edge devices | B | 87.9 | 2017 | Israel | Raised 60M | https://hailo.ai/ | An architecture built from the ground up for AI Mythic has developed a truly unique AI compute platform that enables smart camera systems, intelligent appliances, brilliant robotics, and more. | ||
9 | Mythic | Hardware | Edge devices | B | 85.2 | 2012 | Bay Area | https://www.mythic-ai.com/ | BrainChip brings artificial intelligence to the edge with a high-performance, small, ultra-low power solution that enables continuous learning and inference. | |||
10 | Habana Labs | Hardware | Edge devices | Intel | 75 | 2016 | Israel | https://habana.ai/ | EdgeQ is an information technology company that specializes in the fields of 5G chip systems. | |||
11 | Kneron | Hardware | Edge devices | A | 73 | 2015 | San Diego | Raised 40M + unknown | https://www.kneron.com/ | Ultra-low power consumption AI inference accelerator IP specialized for inference arithmetic processing of CNN that operates as a circuit on FPGA device or ASIC device . | ||
12 | Prophesee | Hardware | Edge devices | C | 65.3 | 2014 | France | https://www.prophesee.ai/ | Accelerate AI, Neuromorphic, AI Chip, Optical Computing, Lightmatter | |||
13 | Syntiant | Hardware | Edge devices | C | 65.1 | 2017 | LA | Raised 35M | https://www.syntiant.com/ | Habana Labs was founded in 2016 to create world-class AI Processors, developed from the ground-up and optimized for training deep neural networks and for inference deployment in production environments. | ||
14 | Blaize | Hardware | Edge devices | C | 65 | 2010 | Sacramento | https://www.blaize.com/ | Hardware is bottlenecked by data movement & compute. We use photonics to solve both | |||
15 | Groq | Hardware | Accelerator | 62.3 | 2016 | Bay Area | Raised unknown | https://groq.com/ | The Next Generation of Computing is here. | |||
16 | EdgeQ | Hardware | Edge devices | A | 53 | 2018 | Bay Area | Raised 51M, out of stealth | https://www.edgeq.io/ | Wave Computing is revolutionizing AI and deep learning with its dataflow-based systems and embedded solutions. | ||
17 | LeapMind | Hardware | Edge devices | C | 50 | 2012 | Tokyo | https://leapmind.io/ | GreenWaves' GAP8 is the industry's first ultra-low-power processor enabling battery-operated AI in IoT applications. | |||
18 | Lightelligence | Hardware | Accelerator | A | 36 | 2017 | Boston | Raised 26M | https://www.lightelligence.ai/ | The World’s Top Performing AI Processor for Edge Devices Hailo offers a breakthrough microprocessor uniquely designed to accelerate embedded AI applications on edge devices. Breathe life into your edge AI product today with Hailo-8. | ||
19 | SiMa.ai | Hardware | Edge devices | A | 30 | 2018 | Bay Area | Raised 30M | https://sima.ai/ | Cambricon Technologies builds core processor chips for intelligent cloud servers, intelligent terminals, and intelligent robots. | ||
20 | BrainChip | Hardware | Edge devices | IPO | 27.8 | 2006 | LA | https://brainchipinc.com/ | Removing the Barrier to Custom Silicon | |||
21 | GreenWaves Technologies | Hardware | Edge devices | A | 12.5 | 2014 | France | Is your ML Green?TM We believe that the future of compute is high performance machine learning at the edge – and today, power is the limiter. | ||||
22 | Luminous Computing | Hardware | Accelerator | A | 9 | 2018 | Bay Area | Raised unknown | https://luminous.co/ | Silicon design reimagined for a compute-intensive world. | ||
23 | Zero ASIC | Hardware | Edge devices | 0 | 2020 | Boston | Changed name from Adapteva | https://www.zeroasic.com/ | Graphcore has built a new type of processor for machine intelligence to accelerate machine learning and AI applications for a world of intelligent machines. | |||
24 | Boulder AI | Hardware | Edge devices | 0 | 2017 | Boulder | https://boulderai.com/ | Kneron develops an application-specific integrated circuit and software that offers artificial intelligence-based tools. |