New Release • Packt Publishing • May 2026
By Ajeet Singh Raina and Harsh Manvar • Packt Publishing • 1st Edition
Just ReleasedDrop your name and email to reveal your exclusive discount code. Offer ends July 17.
Amazon Best Sellers Rank
Live snapshot from Amazon — categories where readers are finding the book.
#28
Software Design Tools
#50
Computer Systems Analysis & Design
#349
Computer Systems Analysis & Design
Best Sellers Rank
#1,717,063 in Kindle Store
Customer Reviews
5.0 · 4 reviews
Cached snapshot · Updated May 28, 2026 · View on Amazon
Why this book
A hands-on book. Every concept lands with runnable Docker commands and working code you can copy today.
Start with a single containerized model, then grow into multi-model, multi-agent workloads on Kubernetes.
Covers the 2026 Docker AI stack: Model Runner, MCP Gateway, Docker Offload, Agents, and Sandboxes.
Written for engineers who operate AI, not just prototype it — real architectures, real production lessons.
About the book
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'll learn
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.
Table of contents
"Highly recommended if you're tired of tutorials that stop at "it ran on my machine.""
I've been running local LLMs and experimenting with agents for a while, but getting them to run reliably at scale has always been a pain. This book finally bridges that gap. The authors blend Docker fundamentals with real AI workflows — not just "here's a Dockerfile," but practical patterns for containerizing LLMs, quantizing models, and serving them efficiently. One of the most practical "LLMOps in the real world" resources I've found.
"Essential AI Guide for 2026"
Operational AI with Docker is not only a hands-on guide, but it's also a journey. You're in for a ride to learn different aspects of agentic AI development. I read the book cover to cover, and it's very rewarding. Ajeet and Harsh need no introduction — they did an awesome job teaching cutting-edge technology and tools that have not yet been discovered by many.
"Strong Practical Guide for AI + Docker Workflows"
This book does a great job of connecting modern AI workflows with real-world Docker and Kubernetes practices. Instead of staying theoretical, it focuses on practical topics like containerizing AI workloads, running models locally, orchestration, observability, and multi-agent systems. The writing is clear and hands-on, and the examples feel relevant to how teams are actually deploying AI services today.
"Great AI deployment with Docker"
A comprehensive, hands-on guide for engineers and developers looking to deploy, scale, and manage AI/ML services using Docker and Kubernetes. Filled with real-world examples, step-by-step tutorials, and configuration samples. An essential resource for anyone responsible for moving AI models from notebooks to scalable, secure, and maintainable production systems.
Community buzz
Feedback from practitioners across LinkedIn and the wider community.
"Most GenAI projects do not break because the model is wrong. They break when you try to run them in the real world. The new book by Docker Captain Harsh Manvar, and Ajeet Singh Raina focuses on that gap."
Docker, Inc
Official Docker page · 826K followers
"As a Docker Captain and MLE, I look for three things in a tech book: code that works, patterns that scale, and security-first thinking. This book hits all three. From running local models with docker model run to orchestrating agent teams on Kubernetes, this is the definitive guide for the next generation of AI infrastructure."
Alagu Prakalya P
Docker Captain · ML Engineer
"The book is about the messy part of AI that nobody really talks about: getting it actually to run in production. If you've ever gotten an AI project working on your laptop and then hit a wall trying to make it real, this book is built for exactly that gap."
Pradumna Saraf
Docker Captain · Technical reviewer
"What I find valuable about the book is that it brings structure to this growing ecosystem and explains where the different tools fit — Docker Model Runner, Docker Offload, the MCP Toolkit and Docker Sandboxes."
Sergei Shitikov
Docker Captain · Lead Software Engineer
"I just finished reading Operational AI with Docker, by Ajeet Singh Raina and Docker Captain Harsh Manvar, published by Packt — and it was an enjoyable read."
Sarthak Varshney
Docker Captain
"A deep dive into Operational AI with Docker — covering Model Runner, MCP Gateway, agents, and sandboxes. A must-read for engineers shipping AI to production."
Hamida Rebai
Docker Captain · Microsoft MVP
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