MLOps Foundations: Model Evaluation with Vertex AI

Training a machine learning model is only half the battle; ensuring it performs reliably on real-world data is where many projects fail. Understanding how to systematically evaluate your models is the key to successful, production-grade MLOps. This text-based course guides you through the essential concepts and workflows of model evaluation. You will transition from manual, ad-hoc testing to automated, robust evaluation processes that give you full confidence in your model's performance before deployment. What you'll learn: Understand core model evaluation metrics for both classification and regression tasks; Configure Vertex AI evaluation tools to automate performance checks; Analyze model bias and fairness indicators to ensure ethical deployments; Track model lineage and metadata to maintain a clear history of experiments; Compare multiple model versions systematically to select the best candidate for production. You will start with foundational evaluation theory and key metrics before moving on to practical configurations. Through structured written explanations and step-by-step setup examples, you will learn how to integrate evaluation seamlessly into your broader MLOps workflow. This course is designed for beginners, aspiring ML engineers, and data scientists looking to standardize their workflow. No advanced machine learning background is required to begin. Start reading today to build reliable, high-performing machine learning pipelines with confidence.