#99 Post-Deployment Data Science

Many machine learning practitioners dedicate most of their attention to creating and deploying models that solve business problems. However, what happens post-deployment? And how should data teams go about monitoring models in production?

is the Co-Founder and CEO of , an open-source python library that allows users to estimate post-deployment model performance, detect data drift, and link data drift alerts back to model performance changes. Originally, Hakim started a machine learning consultancy with his NannyML co-founders, and the need for monitoring quickly arose, leading to the development of NannyML.

Hakim joins the show to discuss post-deployment data science, the real-world use cases for tools like NannyML, the potentially catastrophic effects of unmonitored models in production, the most important skills for modern data scientists to cultivate, and more.