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Anotácia knihy

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.

Parametre knihy

• Celý názov: Practical AI Engineering
• Podnázov: A Hands-on Guide to Building Scalable AI with Python,LLMs, RAG, TensorFlow, PyTorch and Kubernetes - From Scratch to Mastery
• Autor: Juno Darian
• Jazyk: Angličtina
• Väzba: Brožovaná
• Počet strán: 316
• EAN: 9798294142926
• ID: 50696165
• Nakladateľ: Independently published
• Hmotnosť: 737 g
• Rozmery: 280 × 216 × 17 mm
• Dátum vydania: 25. July 2025

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