Modern AI systems don't fail at modeling; they fail in production. Moving from experiments to reliable, scalable systems requires more than notebooks and scripts. It requires infrastructure. Operational AI with Docker shows you how to build, deploy, and operate AI systems that work beyond a single machine. You'll learn how to use Docker as a consistent runtime for machine learning workflows, package models as reproducible artifacts, and run them reliably across environments. Starting with containerized machine learning, you'll progress to model serving, AI deployment, and scalable infrastructure using Kubernetes. You'll implement production-ready patterns for resource management, autoscaling, observability, and performance tuning, ensuring your AI workloads remain stable under real-world conditions. The book goes beyond traditional MLOps by introducing agentic AI systems, including autonomous agents, multi-agent architectures, and secure execution environments. You'll also explore modern integration patterns using the Model Context Protocol (MCP), enabling AI systems to interact safely with tools, APIs, and data sources. By the end of this book, you'll be able to design and operate production AI systems that are reproducible, scalable, and ready for real-world deployment using Docker and Kubernetes.