12-02, 14:30–15:00 (UTC), Talk Track II
Machine learning operations (MLOps) are often synonymous with large and complex applications, but many MLOps practices help practitioners build better models, regardless of the size. This talk shares best practices for operationalizing a model and practical examples using the open-source MLOps framework vetiver to version, share, deploy, and monitor models.
Data scientists understand the pitfalls of building models; concepts such as overfitting have deeply thought out solutions built into data science workflows, but less thought is given to bringing models off a laptop. Adding MLOps practices such as versioning, deploying, and monitoring models avoids the pitfall of having model objects stuck on your personal machine. Building an MLOps strategy can sound daunting for data science teams, but these practices can be used in any size or scale of project (even projects that include multiple languages!) to create robust and reproducible models to be shared with others.
Listeners need no previous MLOps knowledge, but should have basic understanding of a data science or machine learning lifecycle. By the end of this talk, people will understand what the term MLOps entails and how to use MLOps to build better models. Listeners will also walk away with practical knowledge on how to use the open-source MLOps framework vetiver to version, share, deploy, and monitor models in Python, R, or both!
No previous knowledge expected
Isabel Zimmerman is a software engineer on the open source team at RStudio, where she works on building MLOps frameworks. When she's not geeking out over new data science techniques, she can be found hanging out with her dog or watching Marvel movies.