AI Engineer Mastery (Hardcover). Este artículo no está disponible.
Idioma: inglés
Editorial: Notion Press, 2026
- Tapa dura
- Nuevo

Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Vendedor de IberLibro desde 12 de octubre de 2005
Condición: Nuevo
EUR 65,01
Descripción del artículo del vendedor
Hardcover. 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.…
N° de ref. del artículo 9798904511739
- Título
- AI Engineer Mastery (Hardcover)
- Autor
- Jegadeesh M.
- Editorial
- Notion Press
- Año de publicación
- 2026
- Estado
- new
- Encuadernación
- Hardcover
- Idioma
- inglés
- ISBN 13
- 9798904511739
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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