Neuware - Most AI books teach concepts.Very few teach you how to actually build production AI systems from the ground up.You can connect a model to an application.You can even make an AI demo work locally.But building a real AI service is different.Real systems crash. Containers fail. Agents loop endlessly. Memory grows uncontrollably. Infrastructure becomes unstable under load. Suddenly, the challenge is no longer the model itself, it is the architecture surrounding it.This book was written for that reality.Deploying Agentic AI with Docker is a deeply practical, project-focused guide to building and scaling modern AI systems using Docker and Kubernetes. Instead of isolated snippets and disconnected examples, you will work through complete real-world projects designed to reflect how intelligent services are actually engineered and deployed in production environments.Throughout this book, you will build: - Stateful AI agents- Containerized inference services- Multi-service AI architectures- Persistent memory systems- Tool-using autonomous workflows- Scalable API layers- Distributed orchestration pipelines- Production-ready AI deployments with Docker and KubernetesEach project is designed to teach not only how something works, but why modern AI systems are structured the way they are.Along the way, you will uncover: - Why most AI deployments become unstable at scale- How Docker transforms AI deployment workflows- How Kubernetes manages orchestration and resilience- How autonomous agents handle memory, tools, and execution loops- How production AI systems deal with failures, queues, scaling, and observability- How real engineering decisions impact long-term system reliabilityThis is not a book built around theory-heavy explanations or toy chatbot tutorials.It is a hands-on engineering guide for developers who want to move beyond experiments and start building AI infrastructure that can survive real-world pressure.If you are ready to stop building temporary demos and start engineering AI systems that are scalable, resilient, and production-ready, this book will show you how.

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Bibliographic Details

Title: Deploying Agentic AI with Docker : A hands-on guide to building and scaling AI services with Docker and Kubernetes
Publisher: Amazon Digital Services LLC - Kdp Mai 2026
Publication Date: 2026
Language: English
ISBN 13: 9798197107404
Binding: Taschenbuch
Condition: Neu
Dimensions: 254 millimeters width by 178 millimeters height by 21 millimeters depth
Weight: 699 grams

About this title

Synopsis

Most AI books teach concepts.
Very few teach you how to actually build production AI systems from the ground up.
You can connect a model to an application.
You can even make an AI demo work locally.
But building a real AI service is different.
Real systems crash. Containers fail. Agents loop endlessly. Memory grows uncontrollably. Infrastructure becomes unstable under load. Suddenly, the challenge is no longer the model itself, it is the architecture surrounding it.
This book was written for that reality.
Deploying Agentic AI with Docker is a deeply practical, project-focused guide to building and scaling modern AI systems using Docker and Kubernetes. Instead of isolated snippets and disconnected examples, you will work through complete real-world projects designed to reflect how intelligent services are actually engineered and deployed in production environments.
Throughout this book, you will build:

Stateful AI agents
Containerized inference services
Multi-service AI architectures
Persistent memory systems
Tool-using autonomous workflows
Scalable API layers
Distributed orchestration pipelines
Production-ready AI deployments with Docker and Kubernetes

Each project is designed to teach not only how something works, but why modern AI systems are structured the way they are.
Along the way, you will uncover:

Why most AI deployments become unstable at scale
How Docker transforms AI deployment workflows
How Kubernetes manages orchestration and resilience
How autonomous agents handle memory, tools, and execution loops
How production AI systems deal with failures, queues, scaling, and observability
How real engineering decisions impact long-term system reliability

This is not a book built around theory-heavy explanations or toy chatbot tutorials.
It is a hands-on engineering guide for developers who want to move beyond experiments and start building AI infrastructure that can survive real-world pressure.
If you are ready to stop building temporary demos and start engineering AI systems that are scalable, resilient, and production-ready, this book will show you how.

"About this title" may belong to another edition of this title.

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