Items related to Practical AI Engineering: A Hands-on Guide to Building...
Practical AI Engineering: A Hands-on Guide to Building Scalable AI with Python,LLMs, RAG, TensorFlow, PyTorch and Kubernetes - From Scratch to Mastery - Softcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
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Practical AI Engineering: A Hands-on Guide to Building Scalable AI with Python,LLMs, RAG, TensorFlow, PyTorch and Kubernetes - From Scratch to Mastery
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Practical AI Engineering (Paperback)
Seller: CitiRetail, Stevenage, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798294142926
Quantity: 1 available