About the Book
Build, Deploy, and Scale Real-World AI Systems-From Foundation Models to Full-Stack Production Pipelines Are you ready to move beyond tutorials and toy models into the real world of scalable, production-ready AI?
Practical AI Engineering is your complete, no-fluff, hands-on guide to building modern AI applications from scratch to mastery. Whether you're aiming to become a full-stack AI engineer, deploy cutting-edge LLMs (Large Language Models), or bring real-time Retrieval-Augmented Generation (RAG) systems into production, this book takes you there-step by step.
Written for engineers, ML practitioners, and developers who want more than just theoretical knowledge, this book equips you with battle-tested workflows, system design patterns, and toolchains used by top AI teams.
What You'll Master Inside This Book:
AI Engineering from the Ground Up
- Learn what AI engineering really means: beyond models, into systems
- Master the end-to-end AI lifecycle (Design → Deploy → Maintain)
- Think like a systems engineer for real-world impact
The Full Toolkit for Modern AI Engineers
- Python patterns, TensorFlow vs. PyTorch, FastAPI, HuggingFace, LangChain
- Data pipelines, Docker, Kubernetes, and GitOps workflows
- Experiment tracking, versioning, and CI/CD automation
LLMs, Transformers, and Prompt Engineering in Practice
- Understand how GPT models work and scale
- Use OpenAI APIs and HuggingFace models efficiently
- Apply few-shot, chain-of-thought, and retrieval-augmented strategies
- Implement LLMOps for inference, caching, and cost control
Retrieval-Augmented Generation (RAG) and GraphRAG
- Chunking, embeddings, and vector databases (FAISS, Pinecone, Qdrant)
- Build RAG systems with LangChain, FastAPI, and custom memory
- Go beyond text: create knowledge-augmented LLMs with Neo4j and GraphRAG
- Complete projects: Legal QA bots, research assistants, scalable chatbots
Agentic AI and Multi-Tool Orchestration
- Build agents that use tools like Web Browsing, SQL, and PDFs
- Explore LangChain Agents, OpenAgents, AutoGen frameworks
- Monitor hallucinations, plan actions, and design recovery flows
- Ensure safety, logging, and performance in agentic systems
Production-Ready Deployment with Docker & Kubernetes
- Package LLMs and APIs into portable containers
- Use docker-compose and Helm charts for orchestration
- Deploy scalable clusters with GPU access and autoscaling
- Implement health probes, registries, and versioned microservices
Observability, Evaluation & Continuous Delivery
- Monitor LLM drift, RAG relevance, and real-time model metrics
- Run A/B tests, feedback loops, and prompt re-ranking
- Automate your ML pipelines using GitHub Actions + MLflow
- Set up failover, alerts, and canary deployments
Ethical and Global AI Deployment
- Handle bias, safety, privacy, and data sovereignty
- Harden APIs against adversarial prompts and jailbreaking
- Deploy inclusive systems across global and non-Western contexts
Among others..
BONUS: Companion Project Repositories + Cheat Sheets
Real projects: RAG chatbots, GraphRAG assistants, LLM agents
If you're looking for a deeply practical, industry-relevant, and project-driven book to help you master modern AI engineering-this is it.
Perfect for:
- AI/ML engineers and full-stack developers
- Backend engineers diving into LLMs and RAG
- Technical founders building AI-powered products
Join the future of AI development - become a practical AI Engineer.
