Building Machine Learning Models into Docker Images

In this talk, we deep dive into building ML models into container images so that you can run them in production for inference. There are various questions around doing this: Who should build the images and when? What should they contain? How should data science & ML teams interact with DevOps teams? If you build images specific to one platform, will you get locked in? If you try to build your containers inside a container, what happens, and why is this a security challenge? Based on Luke's experience setting up ML container builds for many clients, he'll propose a set of best practices for ensuring secure, multi-tenant image builds that avoid lock-in, and he'll also share some tooling (chassis.ml) and a standard (openmodel.ml) Luke proposes for doing this.