Model Evaluation in MLOps with Vertex AI

Building machine learning models is only half the battle; ensuring they perform reliably in production is where many systems fail. This text-based course guides you through the essential practices of Machine Learning Operations (MLOps) with a strict focus on model evaluation. You will learn how to systematically assess your models to guarantee accuracy, fairness, and safety before deployment. By completing this course, you will gain the skills to set up structured evaluation workflows for both traditional predictive algorithms and modern generative AI models. You will understand how to interpret performance data and make informed decisions about model readiness. What you'll learn: - Understand foundational MLOps principles and the critical role of continuous evaluation. - Evaluate predictive models using key performance metrics such as precision, recall, and ROC curves within Vertex AI. - Assess generative AI models using modern evaluation frameworks, including semantic similarity and safety filters. - Configure automated evaluation pipelines to track model performance over time. - Analyze evaluation reports to identify model drift, bias, and areas for optimization. This course begins with essential definitions and core evaluation concepts before guiding you through step-by-step written explanations of Vertex AI workflows. It is designed for beginner machine learning practitioners, data scientists, and aspiring MLOps engineers who want to understand the production lifecycle. No prior experience with Vertex AI is required. Start reading today to build reliable, production-ready machine learning systems.