Preventing Stale Models in Production


Deploying a machine learning model production is not the end of the project. You have to constantly monitor the model for model drift and the underlying data drift that causes it. That means you have to re-train your model on new datasets often.

In this talk, we'll cover how you can use DVC to track all of the changes to your dataset across each model that gets trained and deployed to production. You’ll see how to reproduce experiments and how you can share experiments and their results with others on your team. By the end of the talk, you should feel comfortable switching between datasets as you keep your model up to date.


Milecia is a senior software engineer, international tech speaker, and mad scientist that works with hardware and software. She will try to make anything with JavaScript first. In her free time, she enjoys learning random things, like how to ride a unicycle, and playing with her dog.

Open Data Science




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