ML (O)Ops! Keeping Track of Changes (Part 2)
With Gaurav Sood The first part of the series, “Improving and Deploying On-Device Models With Confidence,” is posted here. Tracking Changes in ML Systems ML Engineers spend a lot of time keeping track of changes. The changes can be to the model architecture, hyperparameters, training data, or the deployment infrastructure. Each of these changes can affect the model’s performance in ways that are hard to predict. And when something goes wrong, it can be hard to figure out which change caused the problem. ...