AI Engineer Mastery (Paperback)
Jegadeesh M.
Sold by Grand Eagle Retail, Bensenville, IL, U.S.A.
AbeBooks Seller since October 12, 2005
New - Soft cover
Condition: New
Ships within U.S.A.
Quantity: 1 available
Add to basketSold by Grand Eagle Retail, Bensenville, IL, U.S.A.
AbeBooks Seller since October 12, 2005
Condition: New
Quantity: 1 available
Add to basketPaperback. Everyone is "doing AI." Most are still copy-pasting tutorial code that breaks the moment a real user touches it. AI Engineer Mastery is the bridge - a pragmatic, opinionated, code-heavy field guide that takes you from your first genai.Client() call to deploying production AI systems with monitoring, evaluation, and safety guardrails.Written by Jegadeesh M, an AI Researcher who's shipped real AI features at scale, this book skips the theory and goes straight to what works: the exact tools, patterns, and workflows used by engineers at companies building with LLMs every day.The complete journey, in 43 chapters: Foundations - Streaming APIs, LangChain, memory, vector databases, RAGAgents - ReAct, MCP, CrewAI, AutoGen, LangGraph, Google ADKProduction - Fine-tuning with LoRA, advanced RAG (hybrid search, reranking, RAGAS), MLOps with MLflow and LangSmith, cloud deployment on Vertex AI / Bedrock / WatsonX. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Seller Inventory # 9798904511722
Everyone is "doing AI." Most are still copy-pasting tutorial code that breaks the moment a real user touches it. AI Engineer Mastery is the bridge - a pragmatic, opinionated, code-heavy field guide that takes you from your first genai.Client() call to deploying production AI systems with monitoring, evaluation, and safety guardrails.Written by Jegadeesh M, an AI Researcher who's shipped real AI features at scale, this book skips the theory and goes straight to what works: the exact tools, patterns, and workflows used by engineers at companies building with LLMs every day.The complete journey, in 43 chapters: Foundations - Streaming APIs, LangChain, memory, vector databases, RAGAgents - ReAct, MCP, CrewAI, AutoGen, LangGraph, Google ADKProduction - Fine-tuning with LoRA, advanced RAG (hybrid search, reranking, RAGAS), MLOps with MLflow and LangSmith, cloud deployment on Vertex AI / Bedrock / WatsonX.
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