12-02, 13:30–15:00 (UTC), Workshop/Tutorial II
Have you ever wondered what it takes to build a production grade Machine Learning platform? With so many OSS tools and frameworks it can get overwhelming at times how to make everything work. In this workshop we will build a production grade Model training, Model Serving, Model Monitoring platform on AWS EKS. Nothing will be local. These ideas can serve ML Engineers, Applied Data Scientists & Researchers to further extend them and develop a holistic picture of building an ML Platform on OSS.
Building a Machine Learning Platform takes many months and years of effort from multiple engineers. There are several components of ML Platform e.g. an environment to explore and train models, design pipelines that are reproducible, store model artifacts, deploy models on scalable infrastructure such as kubernetes, once deployed monitor the models, automated retraining etc. I have been building some of these components as a ML platform Engineer for over 3 years. I am passionate about learning and using OSS to build ML Platform components on Kubernetes and share with the community. While 90 minutes will never be enough to build each and every component, however in this talk we will share with Engineers and Scientists how we can build some of the critical pieces using OSS. We will walk through a use case of setting up a training pipeline, experiment tracking, model registry & model deployer on AWS EKS. We will be solely using OSS frameworks running on Kubernetes on AWS. It will be mostly a live demo and codes and instructions will be shared as a GitHub repo and Instruction document.
Previous knowledge expected
Machine Learning Platform Engineer at Lyft Inc.