MLOps and Model Evaluation with Vertex AI

Deploying a machine learning model is only the first step; ensuring it performs consistently and accurately in production is the real challenge. Without structured evaluation, identifying bias, drift, and performance degradation becomes nearly impossible. This text-based course guides you through the core principles of Machine Learning Operations (MLOps) with a specific focus on model evaluation using Vertex AI. You will transition from training simple models to systematically assessing, comparing, and monitoring their performance using modern cloud-based tools. What you'll learn: - Understand foundational MLOps concepts, model lifecycle stages, and key evaluation terminology. - Configure Vertex AI evaluation pipelines to automate performance tracking. - Analyze evaluation metrics for classification, regression, and modern generative AI models. - Identify model drift and data bias to maintain high prediction quality over time. - Compare champion and challenger models to make informed deployment decisions. The curriculum begins with essential terminology and the conceptual framework of MLOps. You will then progress through step-by-step written guides and structured code snippets that demonstrate how to run evaluation jobs, interpret performance reports, and integrate evaluation into continuous delivery pipelines. This course is designed for aspiring ML engineers, data scientists, and technical analysts who are new to MLOps, requiring no prior cloud deployment experience. Start reading today to build reliable, production-ready machine learning workflows.