Vertex AI Model Evaluation and MLOps for Beginners

Deploying machine learning models to production is only half the battle; ensuring they remain accurate, unbiased, and reliable over time is where true MLOps begins. This course guides you through the essential principles of model evaluation using Vertex AI. You will transition from simply training models to systematically assessing their performance. By reading through our structured text lessons, you will learn how to set up evaluation pipelines, interpret key metrics for both traditional predictive algorithms and modern generative AI models, and make data-driven deployment decisions. What you'll learn: Understand foundational MLOps terminology and the core lifecycle of model evaluation; Evaluate predictive models using classic metrics like precision, recall, and confusion matrices; Assess generative AI and large language models using modern evaluation frameworks; Configure automated evaluation pipelines within the Vertex AI ecosystem; Identify and mitigate bias to ensure fairness and safety in your model outputs; Apply systematic testing practices to compare model versions before production release. The course begins with essential definitions and foundational concepts of model tracking, then transitions into hands-on text-based walkthroughs of evaluation configurations and metric analysis. This course is designed for aspiring ML engineers, data scientists, and technical product managers who are new to MLOps. No prior production deployment experience is required, though a basic understanding of machine learning concepts is helpful. Start reading today to build reliable, high-quality machine learning systems with confidence.