Training, Evaluating, and Monitoring Machine Learning Models

Building machine learning models is only the first step. To create reliable ML systems, engineers must evaluate model performance, diagnose prediction errors, and monitor deployed models over time. In this course, you'll learn how to train, evaluate, and monitor machine learning models using practical engineering techniques. You’ll begin by exploring model training strategies that improve convergence and performance. You’ll analyze training logs, loss curves, and class imbalance effects to understand how models learn and where they struggle. Next, you’ll learn how to evaluate machine learnin…