Run production-grade GenAI workloads by containerizing, serving, and scaling LLMs, agents, and multi-model pipelines with Docker, MCP, and Kubernetes for cloud platforms

Key Features

Deploy and operate local and edge-friendly LLM inference using Docker Model Runner and an OpenAI-compatible API
Orchestrate multi-model and multi-agent workloads with Docker Compose and Kubernetes patterns used by platform teams
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Book Description

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.

What you will learn

Containerize GenAI services using Docker images, registries, and Compose-based deployment stacks
Package and distribute models as OCI artifacts for repeatable builds and controlled promotions across environments
Choose GGUF quantization levels to balance cost, latency, and accuracy for cloud and hybrid runtimes
Serve LLMs via Docker Model Runner with an OpenAI-compatible API suitable for internal platforms
Integrate tools and data securely using MCP and Docker MCP Gateway with least-privilege access patterns

Who this book is for

Cloud 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.

Docker Desktop — The Runtime Foundation for AI/ML Workflows
Understanding AI Models in Docker
Model Service with Docker Model Runner
Docker Offload for AI and ML Workflows
Running ML Container Models on Kubernetes
Protocol-Based AI Integration with MCP
Building Autonomous AI Agents
Multi-Model and Multi-Agent Architectures
Advanced Agent Orchestration

About the Author

Ajeet Singh Raina is a Developer Advocate at Docker and an early Docker adopter who has authored 600+ blogs on containerization, cloud-native technologies, and DevOps. He leads a large Docker community ecosystem and organizes initiatives such as Kubetools, sharing practical guidance across Docker, Kubernetes, IoT, and AI/ML operations.

Harsh Manvar is a Senior Software Engineer with over a decade of experience in softwareengineering and DevOps. A Docker Captain, Google Developer Expert, CNCF Ambassador, and Google Champion Innovator, he focuses on building scalable, reliable cloud-native systems and is a top contributor in the Kubernetes space on Stack Overflow.

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