A/B Testing Machine Learning Models in Production

A/B Testing Machine Learning Models in Production

In this guide, I want to explain both the how and why of our approach, and hopefully, give you a better way to use A/B testing machine for test your models in production.

There is (rightfully) quite a bit of emphasis on testing and optimizing models pre-deployment in the machine learning ecosystem, with meta machine learning platforms like Comet becoming a standard part of the data science stack.

There has been less of an emphasis, however, on testing and optimizing models post-deployment, at least as far as tooling is concerned. This dearth of tooling has forced many to build extra in-house infrastructure, adding yet another bottleneck to deploying to production.

We’ve spent a lot of time thinking about A/B testing deployed models in Cortex, our open source ML deployment platform. After several iterations, we’ve built a set of features that make it easy to conduct scalable, automated A/B tests of deployed models. In this guide, I want to explain both the how and why of our approach, and hopefully, give you a better way to test your models in production.

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