Vertex AI Model Evaluation for MLOps

Deploying a machine learning model is only half the battle; knowing how well it performs in the real world is what determines its long-term success. This text-based course introduces you to the essentials of model evaluation within the Vertex AI ecosystem. You will learn how to systematically analyze model performance, identify bias, and implement automated evaluation workflows to maintain high-quality predictions. What you'll learn: - Understand core model evaluation metrics for classification, regression, and forecasting tasks - Configure Vertex AI evaluation pipelines to automate performance tracking - Analyze model behavior using feature attribution and explainable AI concepts - Detect data drift and performance degradation in production environments - Apply bias detection techniques to ensure fair and ethical machine learning outcomes The course begins with foundational concepts of model validation before guiding you through practical configuration files and evaluation workflows using Vertex AI. You will read detailed explanations and analyze structured code snippets to build confidence in your MLOps skills. This course is designed for beginners, aspiring ML engineers, and data scientists, with no advanced MLOps experience required. Start reading today to master the art of model evaluation in Vertex AI.