MLOps with Vertex AI: Model Evaluation Fundamentals

Deploying a machine learning model is only half the battle; ensuring it performs consistently and accurately in production is where true MLOps begins. This text-based course guides you through the essential principles of model evaluation and validation using Vertex AI. You will transition from training basic models to systematically assessing their performance, identifying bias, and setting up robust evaluation pipelines. By understanding how to interpret key metrics and automate validation, you will gain the confidence to deploy reliable models that drive real-world value. What you'll learn: Understand core MLOps concepts and the model lifecycle within Vertex AI; Evaluate machine learning models using classification, regression, and custom metrics; Identify and mitigate model bias and data drift to maintain post-deployment accuracy; Configure automated evaluation pipelines to streamline your development workflow; Analyze model performance reports to make data-driven deployment decisions. The course starts with foundational MLOps terminology and evaluation theory before walking you through step-by-step written guides on configuring Vertex AI evaluation tools and analyzing performance metrics. Designed for aspiring ML engineers, data scientists, and technical analysts new to MLOps, this course requires no prior experience with Vertex AI, though a basic understanding of machine learning concepts is helpful. Start reading today to build more reliable and robust machine learning workflows.