Operational AI with Docker

Operational AI with Docker: A Practical Path from Containers to Agentic Services 💻

Docker changed how software is built and shipped. Now it is changing how AI models and agentic services are packaged, deployed, and operated. Operational AI with Docker by Ajeet Singh Raina and Harsh Manvar is a hands-on guide for engineers who need to move AI workloads beyond a local notebook and into reliable, scalable environments.

What This Book Covers

Published by Packt Publishing, this 2026 title focuses on the operational side of AI. Rather than treating models as isolated experiments, it shows how containers, images, registries, and orchestration fit into the lifecycle of modern AI services.

• Docker Desktop foundations for AI/ML workflows, including installation, verification, resource settings, and smoke tests.
• Core Docker commands such as run, build, ps, images, and logs, with attention to layer caching,.dockerignore, and debugging build failures.
• AI model distribution through OCI artifacts, model cards, tags versus digests, GGUF files, quantization, context size, and storage management.
• Docker Compose for multi-service AI applications, including explicit model access, runtime knobs, environment management, and naming conventions.
• Deployment and scaling concepts for agentic AI services using Docker and Kubernetes, as outlined in the book’s subtitle.

Who Will Benefit 🎯

This book is aimed at developers, DevOps engineers, platform engineers, and MLOps practitioners who already understand the basics of software delivery and want to apply those skills to AI systems. It is also useful for teams moving from local prototypes to shared, reproducible AI services. The authors bring deep Docker and Kubernetes community experience, and the material reflects that practical, operator-first perspective.

Inside the Early Chapters

The opening chapters build a strong foundation. You start with Docker Desktop as the runtime environment for AI/ML work, then move into how images, containers, and registries actually behave. The discussion of tags, digests, image layers, and registry workflows is especially relevant for teams that need reproducibility. Later, the focus shifts to model packaging and distribution, where choices about quantization, context size, and storage can have a direct impact on performance and cost.

Why This Matters for AI Operations ⚙️

AI services often fail not because the model is wrong, but because the surrounding system is fragile. Containerization helps make dependencies explicit, environments repeatable, and deployments portable. When you combine Docker with Kubernetes, you gain a path to scale agentic services while keeping operations manageable. This book speaks to that reality: it treats AI as a production workload, not just a research exercise.

Practical, Reproducible, and Built for Real Teams

Expect step-by-step guidance, checklists, and operational patterns rather than abstract theory. The book emphasizes reproducible workflows, image size awareness, disk hygiene, and configuration management. For readers who want to run ML stacks without installing everything locally, build multi-service AI applications, or prepare models for team-wide use, the material stays focused on what works in practice.

Add It to Your Technical Library 📚

Operational AI with Docker is a timely resource for engineers navigating the intersection of containerization, cloud-native infrastructure, and AI. If your work involves deploying models, managing AI services, or building the platform layer beneath intelligent applications, this Digital Delights ebook edition gives you a detailed, practical guide to the operational side of the stack.