Automate, Optimize, and Monitor ML Models

Machine learning models lose accuracy over time without proper monitoring and optimization. This Short Course was created to help ML and AI professionals build robust, production-ready systems that maintain performance at scale. By completing this course, you'll master critical MLOps skills for detecting model drift, implementing automated retraining workflows, and creating optimized ML pipelines that ensure sustained business value in production environments. By the end of this course, you will be able to: - Evaluate production model performance to detect and mitigate drift - Create an automated, end-to-end machine learning pipeline for model optimization This course is unique because it bridges the gap between model development and production operations, focusing on automation and monitoring strategies that prevent costly model failures. To be successful in this project, you should have experience with machine learning fundamentals and Python programming

• Evaluate production model performance to detect and mitigate drift
• Create automated end-to-end machine learning pipelines for model optimization
• Apply automated tuning techniques to improve model performance consistently
• Monitor ML models continuously to maintain sustained business value

• Basic familiarity with machine learning concepts and terminology
• Readiness for hands-on practice through applied exercises