Model Evaluation and MLOps with Vertex AI

Deploying machine learning models is only half the battle; ensuring they remain accurate, unbiased, and reliable in production is where true MLOps begins. This text-based course guides you through the essential principles of model evaluation, helping you build robust systems that deliver consistent real-world value. You will transition from understanding basic model performance to implementing advanced evaluation workflows. Through clear written explanations and practical code examples, you will gain the skills needed to confidently assess both predictive and generative AI models using industry-standard metrics and tools. What you'll learn: Understand the core concepts of MLOps and the critical role of model evaluation; Evaluate predictive models using classification, regression, and forecasting metrics; Assess generative AI and large language models (LLMs) using modern evaluation frameworks; Detect model drift, bias, and performance degradation in production environments; Configure evaluation pipelines within Vertex AI to automate quality checks; Apply modern observability practices to monitor model health and data quality over time. The course begins with foundational definitions of key MLOps terminology and evaluation metrics, before moving step-by-step into practical evaluation workflows and automated pipelines. This course is designed for aspiring ML engineers, data scientists, and developers who are new to MLOps and want to master model evaluation with no prior cloud operations experience. Start reading today to build reliable, high-quality machine learning systems with confidence.