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Docker for AI Engineers: Build, Deploy, and Scale LLM Applications with Docker, Containers, and Modern DevOps
Paperback
Series: The AI Engineers, Book 1
ProgrammingSystem Administration
Publisher: Independently Published
Published: Dec 9 2025
Pages: 280
Weight: 1.44
Height: 0.59 Width: 8.50 Depth: 11.00
Language: English
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.
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
- 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
- One-command LLM execution
- Memory, quantization & performance tuning
- CPU vs GPU tradeoffs
- Real-world chatbot, embeddings, and inference use cases
- LLM + Vector Database + API backend stacks
- Docker Compose for microservices
- Environment variables, secrets, and secure deployment
- Logging, monitoring, and debugging distributed systems
- Milvus, Chroma, Weaviate, Postgres, Redis
- Persistent storage and data scaling
- RAG architecture in production containers
- Automated image builds & deployments
- Docker Scout, vulnerability scanning & hardening
- Logging, metrics, tracing & performance monitoring
- Secrets management and zero-trust container security
- Cross-platform builds: CUDA, ROCm, and WASM
- Autoscaling and load balancing LLM inference
- Distributed inference & production-grade APIs
- Troubleshooting bottlenecks and optimizing performance
- WASM-based AI workloads
- Lightweight models for edge devices
- Serverless containers & ultra-fast inference
- 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
- 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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