Automate, Optimize, and Monitor ML Models
By External Provider
About this course
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.
What you will learn
- Production ML systems require continuous monitoring and automated responses to maintain business value over time.
- Drift detection is essential for identifying when models need retraining before performance degradation impacts business outcomes.
- End-to-end automation reduces manual errors and enables scalable ML operations across multiple models and environments.
- Automated tuning techniques help models improve consistently without manual trial-and-error.