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267235786
Operational AI with Docker: Deploy, scale, and operate agentic AI services with Docker and Kubernetes
Item #:
267235786
BDT 7657
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Operational AI with Docker shows you how to build, deploy, and operate AI systems that work beyond a single machine.
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What Stands Out
Scalable Deployment
Leverage Docker and Kubernetes for efficient scaling of AI services, ensuring performance remains optimal even with increasing workloads.
Simplified Management
Streamline operations with container orchestration that simplifies the deployment, management, and monitoring of AI applications.
Agentic AI Services
Enable intelligent and responsive AI solutions that adapt and learn, providing users with customized experiences and improved problem-solving capabilities.
Product Details
- 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.Table of ContentsDocker Desktop — The Runtime Foundation for AI/ML WorkflowsUnderstanding AI Models in DockerModel Service with Docker Model RunnerDocker Offload for AI and ML WorkflowsRunning ML Container Models on KubernetesProtocol-Based AI Integration with MCPBuilding Autonomous AI AgentsMulti-Model and Multi-Agent ArchitecturesAdvanced Agent Orchestration
| Publisher | Packt Publishing |
| Publication date | 29 April 2026 |
| Language | English |
| Print length | 390 pages |
| ISBN-10 | 1807301095 |
| ISBN-13 | 978-1807301095 |
| Item weight | 667 g |
| Dimensions | 19.05 x 2.24 x 23.5 cm |
Who Should Buy?
-
AI Developers
Developers looking to deploy AI models efficiently using Docker and Kubernetes will find this product invaluable.
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DevOps Professionals
DevOps teams managing scaling and operations of AI services will benefit greatly from the operational insights offered.
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Startups
Startups aiming to leverage AI technology for their products can streamline deployment processes with this operational guide.
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Non-Technical Users
Individuals without a technical background in AI or DevOps may struggle to grasp the content and implementation.
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AI & Machine Learning Editorial Review
Operational AI With Docker: Deploy Scale And Operate Agentic AI Services With Docker And Kubernetes is a comprehensive guide published by Packt Publishing, aimed at professionals looking to leverage Docker and Kubernetes for operationalizing AI services. This 390-page book, released on 29 April 2026, delves into effective deployment strategies and scaling methods essential for an AI-driven environment. Many readers appreciate the depth of knowledge provided and find the step-by-step instructions particularly helpful in navigating complex AI integrations. The clear layout and well-structured content make it accessible for both beginners and experienced practitioners looking to enhance their skills in operational AI.
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Pros
- In-depth insights into operating AI with Docker
- Comprehensive guide with step-by-step instructions
- Suitable for both beginners and experienced practitioners
- Well-structured content for easy understanding
- Covers deployment strategies and scaling methods
Cons
- Limited information on real-world case studies
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Features & Benefits
- Learn to build scalable AI systems using Docker and Kubernetes.
- Containerize GenAI services for reliable, production-grade deployments.
- Implement advanced resource management and performance tuning patterns.
- Explore multi-agent architectures and modern integration patterns.
- Gain practical insights for deploying AI workloads in diverse environments.
- Perfect for cloud engineers, DevOps, and platform teams focused on AI.
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