Vertex AI MLOps: Model Evaluation and Monitoring

Deploying machine learning models is only half the battle; ensuring they remain reliable, accurate, and fair in production is where true MLOps begins. This text-based course guides you through the foundational concepts of model evaluation and monitoring on Vertex AI. You will transition from training simple models to managing robust, production-ready AI lifecycles. By reading through practical scenarios, you will understand how to systematically assess model performance, detect drift, and implement continuous evaluation strategies for both traditional predictive algorithms and modern generative AI models. What you'll learn: Understand foundational MLOps principles and Vertex AI core services; Evaluate predictive model performance using key metrics like precision, recall, and ROC curves; Assess generative AI and large language models using modern evaluation frameworks; Configure continuous monitoring to detect data drift and concept drift in production; Implement automated evaluation pipelines to streamline model selection and optimization; Apply best practices for model versioning, lineage tracking, and governance. The course starts with essential terminology and foundational MLOps concepts before moving into step-by-step written walkthroughs of Vertex AI tools. You will practice through structured written exercises and conceptual scenarios designed to build real-world decision-making skills. This course is designed for beginner data scientists, aspiring MLOps engineers, and software developers looking to understand model lifecycle management, with no prior cloud engineering experience required. Start reading today to build reliable, production-grade machine learning pipelines with confidence.