Automate, Optimize, and Monitor ML Models

Overview

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.

Syllabus

Module 1: Production Model Performance Evaluation and Drift Detection

Learners will master the systematic evaluation of production ML models to identify performance degradation and implement drift detection systems that automatically trigger remediation actions.

Module 2: Automated ML Pipeline Creation and Optimization

Learners will build comprehensive automated ML pipelines with integrated hyperparameter optimization and end-to-end automation that maintains model performance in production environments.