AI4Good: ML/AI services, tools, training resources

	A	B	C	D
1	Service	Developed by	Description/Applications	Links

2	Machine Learning Studio	Microsoft	Machine Learning Studio is a powerfully simple browser-based, visual drag-and-drop authoring environment where no coding is necessary. Go from idea to deployment in a matter of clicks.	https://azure.microsoft.com/en-us/services/machine-learning-studio/
3	Azure Cognitive Services	Microsoft	Azure Cognitive Services are APIs, SDKs, and services available to help developers build intelligent applications without having direct AI or data science skills or knowledge. Azure Cognitive Services enable developers to easily add cognitive features into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure Cognitive Services can be categorized into five main pillars - Vision, Speech, Language, Web Search, and Decision.	https://azure.microsoft.com/en-us/services/cognitive-services/
4	Azure Machine Learning service	Microsoft	Rapidly build and deploy machine learning models using tools that meet your needs across skill levels, from no-code to code-first experiences. Use a visual drag-and-drop interface, a hosted notebook environment, or automated machine learning. Accelerate model development with automated feature engineering, algorithm selection, and hyperparameter sweeping. Get built-in support for familiar open-source tools and frameworks, including ONNX, Python, PyTorch, scikit-learn, and TensorFlow.	https://azure.microsoft.com/en-us/services/machine-learning-service/
5	Bot Framework	Microsoft	Build conversational AI experiences for your organization Azure Bot Service enables you to build intelligent, enterprise-grade bots with ownership and control of your data. Begin with a simple Q&A bot or build a sophisticated virtual assistant. Use comprehensive open source SDK and tools to easily connect your bot to popular channels and devices. Give your bot the ability to speak, listen, and understand your users with native integration to Azure Cognitive Services.	https://dev.botframework.com/
6	Power Virtual Agents	Microsoft	Offering that enables anyone to create powerful chatbots using a guided, no-code graphical interface. You can create an engaging, interactive experience without the need for AI experts or developers. All you need is an understanding of your users and content for the bot experience to engage more deeply with your audience.	https://powervirtualagents.microsoft.com/
7	Watson OpenScale	IBM	Watson OpenScale helps organizations, especially in regulated industries like finance, healthcare and governments, to validate and monitor their AI models to ensure fair and explainable outcomes. The tool is built for business stakeholders to get visibility into how an AI model is behaving by explaining its decisions, presenting conditions under which a model's decisions may change and to actively detect and mitigate biased model behavior to enforce business fairness. It also captures a broad set of metrics which can help identify performance issues and their impact on business outcomes. Users can validate or monitor models built in any open source framework and hosted in IBM's platforms, 3rd party engines or their own custom ML runtimes.	https://www.ibm.com/cloud/watson-openscale/
8	AI Hub	Google	AI Hub is a hosted repository of plug-and-play AI components, including end-to-end AI pipelines and out-of-the-box algorithms. AI Hub provides enterprise-grade sharing capabilities that let organizations privately host their AI content to foster reuse and collaboration among machine learning developers and users internally.	https://cloud.google.com/ai-hub/
9	Cloud AutoML	Google	Cloud AutoML is a suite of machine learning products that lets developers with limited ML expertise train high-quality models specific to their needs. Cloud AutoML leverages more than ten years of proprietary Google Research technology to help your machine learning models achieve faster performance and more accurate predictions.	https://cloud.google.com/automl/
10	Structured Data	Google
11	AutoML Tables		Train a model on tabular data with minimal effort.	https://cloud.google.com/automl-tables/
12	Recommendations AI		Easily create a recommendation system.	https://cloud.google.com/recommendations-ai/docs/
13	BigQuery ML		Create an ML model using SQL queries.	https://cloud.google.com/bigquery/#bigqueryml
14	Sight	Google
15	Vision AI		Detect and classify objects in images.	https://cloud.google.com/vision/
16	Video AI		Detect and classify objects in videos, caption videos.	https://cloud.google.com/video-intelligence/
17	AutoML Vision		Easily create a custom object detection model.	https://cloud.google.com/vision/automl/docs
18	AutoML Video Intelligence		Easily create a custom video labelling model.	https://cloud.google.com/video-intelligence/automl/docs
19	Language	Google
20	Natural Language AI		Parse entities and sentiments from text.	https://cloud.google.com/natural-language/
21	Translation AI		Translate text and audio between langauges.	https://cloud.google.com/translate/
22	AutoML Natural Language		Easily create a custom text classification model.	https://cloud.google.com/natural-language/automl/docs/
23	AutoML Translation		Easily create a custom translation model.	https://cloud.google.com/translate/automl/docs
24	Converstation AI	Google
25	Cloud Speech-to-Text API		Convert speech to text in many languages.	https://cloud.google.com/speech-to-text/
26	Cloud Text-to-Speech API		Convert text to speech in many languages.	https://cloud.google.com/text-to-speech/
27	Dialogflow		Create voice and text-based conversational chat bots.	https://cloud.google.com/dialogflow/
28	AI Platform	Google		https://cloud.google.com/ai-platform/
29	Kubeflow		A platform for training machine learning models in any environment.	https://www.kubeflow.org/
30	Cloud TPU		Specialized hardware that allows you train ML models faster.	https://cloud.google.com/tpu/
31	AI Platform Training		A platform for training machine learning models.	https://cloud.google.com/ml-engine/docs/training-overview
32	Deep Learning Containers		Ready-made computing environments for deep learning.	https://cloud.google.com/ai-platform/deep-learning-containers/
33	Data Labeling Service		A service that allows you to crowdsource labelling of your data.	https://cloud.google.com/data-labeling/docs/
34	AI Platform Notebooks		Managed notebook environments for data science and machine learning.	https://cloud.google.com/ai-platform-notebooks/
35	Deep Learning VM Image		Ready-made computing environments for deep learning.	https://cloud.google.com/deep-learning-vm/
36	Machine Learning on AWS	AWS	Machine Learning on AWS Overview	https://aws.amazon.com/machine-learning/
37	Machine Learning on AWS Training	AWS	Dive deep into the same machine learning (ML) curriculum used to train Amazon’s developers and data scientists with 30+ digital ML courses totaling 45+ hours, plus hands-on labs and documentation, originally developed for Amazon's internal use. Developers, data scientists, data platform engineers, and business decision makers can use this training to learn how to apply ML, artificial intelligence (AI), and deep learning (DL) to their businesses unlocking new insights and value.	https://aws.amazon.com/training/learning-paths/machine-learning/
38	Example use cases for Machine Learning	AWS	Explore how machine learning is being used to to improve the quality of healthcare, fight human trafficking, provide better customer service, and protect you from fraud	https://aws.amazon.com/machine-learning/customers/
39	AI Services	AWS	Artificial intelligence (AI) can enhance the customer experience in a contact center, automate content moderation in media, improve healthcare analytics, forecast demand more accurately, and much more.	https://aws.amazon.com/machine-learning/ai-services/
40	Sagemaker	AWS	Machine learning for every developer and data scientist. Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.	https://aws.amazon.com/sagemaker/
41	Sagemaker Tutorial	AWS	Learn how to get started with Amazon SageMaker in 10 minutes	- https://aws.amazon.com/getting-started/tutorials/build-train-deploy-machine-learning-model-sagemaker/
42	ML notebooks on GitHub	AWS	Use Amazon SageMaker notebook instances on Github for a wide range of use cases and machine learning workflows	https://aws.amazon.com/blogs/machine-learning/how-to-use-common-workflows-on-amazon-sagemaker-notebook-instances/
43	Learn ML with DeepRacer	AWS	WS DeepRacer is the fastest way to get rolling with machine learning, literally. Get hands-on with a fully autonomous 1/18th scale race car driven by reinforcement learning, 3D racing simulator, and global racing league.	https://aws.amazon.com/deepracer/
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