Eugene Yan • 5/25/2020

A Practical Guide to Maintaining Machine Learning in Production

This article provides a practical guide for maintaining machine learning systems in production. It covers essential practices like monitoring training and serving data for contamination, tracking model behavior, simplifying engineering to reduce operational burden, minimizing feedback loops and bias, structuring teams for efficiency, and handling customer complaints. The author outlines specific tools and checks for validating incoming data and ensuring model reliability.

0 comments

#Machine Learning #production #Monitoring