{ "item_title" : "Practical AI Engineering", "item_author" : [" Juno Darian "], "item_description" : "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 contextsAmong others.. BONUS: Companion Project Repositories + Cheat SheetsReal projects: RAG chatbots, GraphRAG assistants, LLM agentsIf 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 productsJoin the future of AI development - become a practical AI Engineer.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/829/414/9798294142926_b.jpg", "price_data" : { "retail_price" : "20.99", "online_price" : "20.99", "our_price" : "20.99", "club_price" : "20.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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