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.
In this episode
Founder, MLOps Consulting
Luke is a passionate technology leader. Experienced in CEO, CTO, tech lead, product, sales, and engineering roles. He has a proven ability to conceive and execute a product vision from strategy to implementation while iterating on product-market fit. Luke has a deep understanding of AI/ML, infrastructure software and systems programming, containers, microservices, storage, networking, distributed systems, DevOps, MLOps, and CI/CD workflows.
Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.