Deploying AI Agents: CI/CD, Versioning, and Evals

Building an AI agent is only the first step; deploying it reliably into production is where the real challenge begins. As AI applications grow more complex, developers need robust systems to test, update, and monitor their agents in the real world. This course provides a structured pathway to operationalize your AI agents. You will start with foundational MLOps concepts and progress through building automated pipelines, versioning your prompts and models, and setting up comprehensive evaluation metrics to ensure your agents perform consistently and safely. What you'll learn: • Understand the foundational concepts of MLOps and agentic AI architecture. • Design CI/CD pipelines specifically tailored for testing and deploying AI agents. • Implement evaluation frameworks (evals) to measure agent accuracy, safety, and performance. • Manage version control for prompts, context data, and model parameters. • Apply modern observability patterns to trace agent decision-making and monitor production health. • Practice deploying robust, production-ready AI workflows through written, step-by-step exercises. The course flows logically from basic terminology and setup to advanced deployment strategies. You will read through clear explanations, explore realistic code snippets, and complete practical written exercises that simulate real-world production environments. Designed for beginners and aspiring AI engineers, this foundational text requires no prior experience with MLOps or complex deployment infrastructure. Start reading today to bridge the gap between AI prototypes and reliable production systems.