Overview

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 Workloads Containers vs VMs vs sandboxes Docker architecture explained clearly Essential commands every AI engineer must know Understanding images, layers, caching, registries & file systems Build Production-Ready AI Containers Write optimized Dockerfiles for LLMs and ML models Use CUDA, cuDNN, ROCm, Python, and AI runtime images Implement multi-stage builds for lightweight deployments Package models, dependencies, and GPU libraries correctly Run Local LLMs Effortlessly Using Docker Model Runner One-command LLM execution Memory, quantization & performance tuning CPU vs GPU tradeoffs Real-world chatbot, embeddings, and inference use cases Design Multi-Container AI Applications LLM + Vector Database + API backend stacks Docker Compose for microservices Environment variables, secrets, and secure deployment Logging, monitoring, and debugging distributed systems Containerize Vector Stores, Databases & RAG Pipelines Milvus, Chroma, Weaviate, Postgres, Redis Persistent storage and data scaling RAG architecture in production containers AI DevOps: CI/CD, Security, Observability & Automation Automated image builds & deployments Docker Scout, vulnerability scanning & hardening Logging, metrics, tracing & performance monitoring Secrets management and zero-trust container security Deploy at Scale with GPUs & Orchestration Cross-platform builds: CUDA, ROCm, and WASM Autoscaling and load balancing LLM inference Distributed inference & production-grade APIs Troubleshooting bottlenecks and optimizing performance The Future: WASM, Edge AI, Micro-Inference & Serverless Containers WASM-based AI workloads Lightweight models for edge devices Serverless containers & ultra-fast inference Who This Book Is For AI Engineers & LLM Developers MLOps and DevOps Engineers Software Engineers transitioning into AI Cloud Engineers & GPU Infrastructure Specialists Technical founders building AI startups Students and professionals breaking into AI engineering Includes Exclusive Appendices Docker CLI cheat sheet for AI Essential AI-ready Dockerfile templates Model Runner configuration samples GPU performance and debugging guide Glossary of AI, Docker, and DevOps terms Recommended tools, frameworks, and learning paths These appendices alone save you months of trial and error.

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