Building Machine Learning Models into Docker Images

Name: Building%20Machine%20Learning%20Models%20into%20Docker%20Images
Uploaded: 2021-08-25T08:00:00.000Z

Posted Aug 25, 2021 | Views 814

# Helix ML

# Tryhelix.ai

Luke Marsden

Founder @ HelixML

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.

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Demetrios Brinkmann

Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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SUMMARY

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.

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