AI Agents and MLOps: Building Production-Ready AI Systems

Artificial intelligence is moving beyond static models to autonomous agents that can plan, use tools, and execute complex workflows. To bring these systems to life, you need to understand both how to build agents and how to deploy them reliably using modern MLOps practices. This text-based course guides you from foundational AI concepts to deploying production-ready agentic workflows. You will gain the confidence to design autonomous systems and establish automated pipelines that keep your models performing at their best. What you'll learn: - Understand the core architecture of AI agents, including planning, memory, and tool integration. - Design autonomous workflows using modern orchestration frameworks and prompt engineering. - Implement foundational MLOps practices to automate model deployment and version control. - Configure continuous monitoring and observability to track agent performance and guardrails. - Manage data pipelines and vector databases for efficient retrieval-augmented generation. - Apply security and ethical best practices to ensure safe and reliable agent operations. You will start by mastering key terminology, agent architectures, and MLOps lifecycles. From there, you will progress through written explanations and structured code snippets that demonstrate how to package, deploy, and monitor your AI systems. This course is designed for software developers, aspiring data scientists, and tech enthusiasts who want to enter the world of production AI. No prior experience with MLOps or agent frameworks is required. Start reading today to transition from building simple scripts to deploying resilient AI agents.