Vertex AI Model Evaluation: MLOps Fundamentals

Evaluating machine learning models is critical to ensuring they perform reliably in production, yet many developers struggle to measure model quality accurately. This text-only course provides a clear pathway to mastering model evaluation within the Vertex AI ecosystem. You will transition from training basic models to systematically assessing both predictive and generative AI models, ensuring they meet real-world performance and safety standards. What you'll learn: Understand foundational evaluation metrics for classification, regression, and generative AI models; Configure Vertex AI Model Evaluation pipelines to automate performance tracking; Compare multiple model versions systematically to select the best candidate for deployment; Evaluate generative AI outputs using modern metrics and LLM-as-a-judge frameworks; Detect bias and drift in production data to maintain model reliability over time; Apply best practices for integrating evaluation steps directly into your MLOps workflows. The course begins with core terminology and evaluation theory before walking you through step-by-step written guides and code snippets for setting up evaluation pipelines in Vertex AI. This course is designed for beginners to MLOps, data scientists, and developers looking to understand model quality assessment, with no prior cloud evaluation experience required. Start reading today to build trust and reliability in your machine learning deployments.