Free Download Practical AI Engineering : A Hands-on Guide to Building Scalable AI with Python,LLMs, RAG, TensorFlow, PyTorch and Kubernetes - From Scratch to Mastery by Juno Darian
English | July 24, 2025 | ISBN: N/A | ASIN: B0FJYGKBLQ | 415 pages | EPUB | 0.38 Mb
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.
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