Validate, Analyze, and Monitor ML Models

This intermediate-level course is designed for machine learning engineers, data scientists, and ML Ops practitioners who are responsible for releasing and maintaining models in production. Building a model is only the beginning. To deliver reliable business value, models must be validated on unseen data, compared against baselines in live environments, and continuously monitored for drift. In this course, The learner will learn how to validate release candidates using hold-out datasets, analyze A/B test and shadow deployment results to quantify performance improvements, and monitor data and prediction drift using practical indicators like PSI. Through short videos, guided coach conversations, and hands-on learning activities, I will practice decision-making that mirrors real production workflows. By the end, The learner will be ready to support safe model releases and long-term model health

• Validate models using hold-out datasets
• Analyze A/B test and shadow deployment results
• Monitor data and prediction drift with practical indicators
• Develop decision-making skills aligned with production workflows

• Basic knowledge of machine learning concepts and monitoring tools
• Ability to engage in hands-on practice