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Artificial Intelligence in Production: A Practitioner's Journey — From Transformers and Large Language Models to Autonomous Agents - Couverture souple
Livre 3 sur 3: The Practitioner's Journey
Synopsis
The third volume of A Practitioner's Journey. Twenty-one chapters cover the modern AI stack end to end — large language models, retrieval-augmented generation, RLHF alignment, autonomous agents, and the LLM-specific infrastructure that surrounds them.
This is not another "build a ChatGPT clone" book. It is a working engineer's guide to the techniques and engineering decisions behind production AI systems. You will start with NLP foundations and tokenization, move through transformer architectures and LLMs, build production-grade RAG pipelines with vector stores and reranking, train reward models and align LLMs with RLHF and DPO, and finish with autonomous agents, the Model Context Protocol (MCP), multi-agent coordination, and multi-modal models.
You will learn to:
- Build and deploy production RAG with vector stores, reranking, and citation grounding
- Fine-tune and align LLMs with RLHF, DPO, and Constitutional AI
- Design autonomous agent loops with safe tool use and approval gates
- Coordinate multiple agents through the Model Context Protocol (MCP)
- Right-size LLM infrastructure across Bedrock, Vertex, Groq, and on-prem
- Distill large models into deployable, edge-ready footprints
Companion volumes: Books 1 and 2 of A Practitioner's Journey cover classical ML, deep learning, and the production engineering stack — recommended as prerequisites if you are new to the field, optional if you already work in ML/AI.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
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Artificial Intelligence in Production: A Practitioner's Journey ? From Transformers and Large Language Models to Autonomous Agents
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Artificial Intelligence in Production
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Artificial Intelligence in Production
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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000. N° de réf. du vendeur L2-9798257184468
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Artificial Intelligence in Production (Paperback)
impression à la demandeVendeur : CitiRetail, Stevenage, Royaume-Uni
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Paperback. Etat : new. Paperback. The third volume of A Practitioner's Journey. Twenty-one chapters cover the modern AI stack end to end - large language models, retrieval-augmented generation, RLHF alignment, autonomous agents, and the LLM-specific infrastructure that surrounds them.This is not another "build a ChatGPT clone" book. It is a working engineer's guide to the techniques and engineering decisions behind production AI systems. You will start with NLP foundations and tokenization, move through transformer architectures and LLMs, build production-grade RAG pipelines with vector stores and reranking, train reward models and align LLMs with RLHF and DPO, and finish with autonomous agents, the Model Context Protocol (MCP), multi-agent coordination, and multi-modal models.You will learn to: Build and deploy production RAG with vector stores, reranking, and citation groundingFine-tune and align LLMs with RLHF, DPO, and Constitutional AIDesign autonomous agent loops with safe tool use and approval gatesCoordinate multiple agents through the Model Context Protocol (MCP)Right-size LLM infrastructure across Bedrock, Vertex, Groq, and on-premDistill large models into deployable, edge-ready footprintsCompanion volumes: Books 1 and 2 of A Practitioner's Journey cover classical ML, deep learning, and the production engineering stack - recommended as prerequisites if you are new to the field, optional if you already work in ML/AI. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9798257184468
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Dharmalingam, K: Artificial Intelligence in Production
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Taschenbuch. Etat : Neu. Neuware - The third volume of A Practitioner's Journey. Twenty-one chapters cover the modern AI stack end to end - large language models, retrieval-augmented generation, RLHF alignment, autonomous agents, and the LLM-specific infrastructure that surrounds them. N° de réf. du vendeur 9798257184468
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