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 learning models using appropriate performance metrics. You’ll analyze confusion matrices and residual patterns to identify systematic prediction errors and assess the statistical significance of model improvements. Finally, you’ll focus on monitoring machine learning models in production environments. You’ll apply validation techniques, analyze A/B testing results, and monitor model behavior over time to detect performance drift and trigger retraining workflows. Through a hands-on project, you'll design a model evaluation and monitoring framework that helps ensure machine learning systems remain accurate and reliable after deployment

• Train machine learning models and analyze training dynamics using logs and loss curves
• Evaluate model performance using metrics, confusion matrices, and statistical analysis
• Design monitoring strategies to detect model drift and maintain model reliability

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work