Docker for AI Engineers: Build, Deploy, and Scale LLM Applications with Docker, Containers, and Modern DevOps

By Raymond Norman

Docker for AI Engineers: Build, Deploy, and Scale LLM Applications with Containers & Modern DevOps
The AI revolution is no longer theoretical-it's happening right now. And at the center of this new era lies a single, unstoppable force transforming how developers build and deploy intelligent systems: Docker for AI & LLM workloads.
Whether you're running local LLMs, building RAG applications, optimizing GPU pipelines, deploying vector databases, or scaling AI microservices, Docker has become the essential foundation of practical AI engineering.
This book is your complete, modern, hands-on guide to mastering that foundation.
What You Will Learn Inside This Book

Master Docker Fundamentals for AI WorkloadsContainers vs VMs vs sandboxesDocker architecture explained clearlyEssential commands every AI engineer must knowUnderstanding images, layers, caching, registries & file systemsBuild Production-Ready AI ContainersWrite optimized Dockerfiles for LLMs and ML modelsUse CUDA, cuDNN, ROCm, Python, and AI runtime imagesImplement multi-stage builds for lightweight deploymentsPackage models, dependencies, and GPU libraries correctlyRun Local LLMs Effortlessly Using Docker Model RunnerOne-command LLM executionMemory, quantization & performance tuningCPU vs GPU tradeoffsReal-world chatbot, embeddings, and inference use casesDesign Multi-Container AI ApplicationsLLM + Vector Database + API backend stacksDocker Compose for microservicesEnvironment variables, secrets, and secure deploymentLogging, monitoring, and debugging distributed systemsContainerize Vector Stores, Databases & RAG PipelinesMilvus, Chroma, Weaviate, Postgres, RedisPersistent storage and data scalingRAG architecture in production containersAI DevOps: CI/CD, Security, Observability & AutomationAutomated image builds & deploymentsDocker Scout, vulnerability scanning & hardeningLogging, metrics, tracing & performance monitoringSecrets management and zero-trust container securityDeploy at Scale with GPUs & OrchestrationCross-platform builds: CUDA, ROCm, and WASMAutoscaling and load balancing LLM inferenceDistributed inference & production-grade APIsTroubleshooting bottlenecks and optimizing performanceThe Future: WASM, Edge AI, Micro-Inference & Serverless ContainersWASM-based AI workloadsLightweight models for edge devicesServerless containers & ultra-fast inferenceWho This Book Is ForAI Engineers & LLM DevelopersMLOps and DevOps EngineersSoftware Engineers transitioning into AICloud Engineers & GPU Infrastructure SpecialistsTechnical founders building AI startupsStudents and professionals breaking into AI engineeringIncludes Exclusive AppendicesDocker CLI cheat sheet for AIEssential AI-ready Dockerfile templatesModel Runner configuration samplesGPU performance and debugging guideGlossary of AI, Docker, and DevOps termsRecommended tools, frameworks, and learning pathsThese appendices alone save you months of trial and error.

• Format: Paperback
• Language: English
• ISBN: B0G64NP8M1
• ISBN13: 9798278112082
• Release Date: December 2025
• Publisher: Independently Published
• Length: 280 Pages
• Weight: 1.44 lbs.
• Dimensions: 0.6" x 8.5" x 11.0"