Operational AI with Docker

Run production-grade GenAI workloads by containerizing, serving, and scaling LLMs, agents, and multi-model pipelines with Docker, MCP, and Kubernetes for cloud platformsKey FeaturesDeploy and operate local and edge-friendly LLM inference using Docker Model Runner and an OpenAI-compatible APIOrchestrate multi-model and multi-agent workloads with Docker Compose and Kubernetes patterns used by platform teamsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionModern 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.What you will learnContainerize GenAI services using Docker images, registries, and Compose-based deployment stacksPackage and distribute models as OCI artifacts for repeatable builds and controlled promotions across environmentsChoose GGUF quantization levels to balance cost, latency, and accuracy for cloud and hybrid runtimesServe LLMs via Docker Model Runner with an OpenAI-compatible API suitable for internal platformsIntegrate tools and data securely using MCP and Docker MCP Gateway with least-privilege access patternsWho this book is forCloud engineers, DevOps engineers, SREs, and platform engineers who need to deploy, operate, and scale GenAI workloads using Docker and Kubernetes on cloud, hybrid, or edge environments. You should be comfortable with the command line and basic service operations; prior Docker or Kubernetes exposure is helpful but not required.]]>