Open source is driving every aspect of the ML model development lifecycle. In this talk, Kris Skrinak explains how to automate the end-to-end process from data ingestion, ETL, feature engineering, algorithm selection and scoring, training, deployment, and continuous integration. Projects covered will include KubeFlow, ML Flow, as AirFlow as well as the ML frameworks SciKit Learn, PyTorch, and Tensorflow. The story is told from the perspective of anecdotal experiences with AWS customers, including what’s working, what’s not, and how AWS customers are pushing the edge in defining long-term solutions.
Presenter: Kris Skrinak, AWS Global Machine Learning Technical Lead