The Importance of Logging and Monitoring in ML Systems

You deploy a model, and it works great—until it doesn’t. I learned the hard way that you can’t just deploy and forget. You need monitoring. Data drift, concept drift, and model decay are real. I set up dashboards to track prediction distributions, latency, and data schema changes. It’s like putting a heart monitor on your model. Without it, you’re flying blind. If the input data changes or the model starts performing poorly, you want to know immediately, not when a customer complains.