Observability in Production: Monitoring Data Drift with WhyLabs and Valohai

Imagine that magical day when your machine learning model is in production. It is possibly integrated into end-user applications, serving predictions and providing real-world value. As a Data Scientist, You may think that your job is done and that you can move on to the next problem to be solved. Unfortunately, the work is just getting started.

What works today might not work tomorrow. And when a model is in real-world use, serving the faulty predictions can lead to catastrophic consequences like what happened with Zillow and their iBuying algorithm which caused the company to overpay for real estate and ultimately, lay off 25% of their workforce.

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We will dig into how we can easily get started with observability and detect data drift using whylogs while executing your pipeline on Valohai.

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