MLOps SF Call for Papers
Have something interesting to say about MLOps? We're waiting for your proposal. 

​Suggested Topics:

Open source technologies for MLOps
KubeFlow standardization
MLRun
MLFlow
Online and offline feature stores
Experiment and data tracking
Reinforced machine learning
AI use-cases in business applications
Machine learning deployment and operation challenges
Machine learning model training at scale
Machine learning pipeline automation
Serverless in machine learning pipelines
Model deployment and monitoring in production
Implementing AI in real-time and interactive applications
Model versioning and reproducibility
Data and feature vector access in production
Security challenges in production
Using GPUs to accelerate training and inferencing
Working in heterogeneous environments (multi-cloud, edge, on-premises)
Distributed machine learning
Spark and big data over Kubernetes
Natural language processing at scale
Cost optimization of machine learning pipelines



Conference date: May 6 2020
Submission deadline: March 16 2020
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