9798299481280 - multi-agent workflows with slms: smarter, faster, and more efficient ai systems di bernie, bernand (6 risultati)
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Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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PAP. Condizione: Used - Very Good. Used - Like New Book. Shipped from UK. Established seller since 2000.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 28,02
EUR 4,86 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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- Print on Demand
Da: California Books, Miami, FL, U.S.A.California Books
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EUR 27,57
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Condizione: New. Print on Demand.
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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EUR 30,83
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Paperback. Condizione: new. Paperback. Artificial intelligence is evolving beyond giant, expensive models. The future lies in Small Language Models (SLMs) - efficient, adaptable systems that can run on modest hardware, deliver faster responses, and integrate seamlessly into production environments. But the real breakthrough come…s when SLMs are orchestrated together into multi-agent workflows.This book provides a comprehensive guide to building, scaling, and governing multi-agent systems with SLMs. Written for engineers, researchers, and applied AI practitioners, it explains how to design efficient workflows where multiple specialized agents collaborate, validate each other, and outperform a single large model.Key areas include: Why SLMs matter: cost and latency trade-offs vs. LLMs, and where they win in practice.Agentic patterns: from ReAct and Reflexion to Graph-of-Thoughts, voting councils, and self-consistency.Architectures: pipelines, hubs, and councils with robust hand-offs, retries, and deadlines.Infrastructure: serving SLMs efficiently with quantization, batching, and optimized runtimes.Memory and retrieval: vector databases, summarization strategies, and privacy-aware storage.Frameworks in action: LangGraph, AutoGen, CrewAI, DSPy, LlamaIndex, smolagents.Evaluation & observability: benchmarks, CI/CD test gates, dashboards, and regression alerts.Governance & safety: guardrails, auditability, and policies for production systems.Case studies & playbooks: real-world workflows in customer support, analytics, CI/CD, and enterprise RAG.Unlike books that focus only on frameworks or introductions to small models, this work is practical and production-oriented. It bridges the gap between theory and deployment, offering actionable patterns, design strategies, and reliability practices that today's AI engineers demand.Whether you are an AI engineer seeking efficiency, a researcher interested in orchestration, or a professional preparing for the shift from monolithic LLMs to collaborative SLM ecosystems, this book gives you the tools to build smarter, faster, and more efficient AI systems. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 31,88
EUR 43,24 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. Artificial intelligence is evolving beyond giant, expensive models. The future lies in Small Language Models (SLMs) - efficient, adaptable systems that can run on modest hardware, deliver faster responses, and integrate seamlessly into production environments. But the real breakthrough come…s when SLMs are orchestrated together into multi-agent workflows.This book provides a comprehensive guide to building, scaling, and governing multi-agent systems with SLMs. Written for engineers, researchers, and applied AI practitioners, it explains how to design efficient workflows where multiple specialized agents collaborate, validate each other, and outperform a single large model.Key areas include: Why SLMs matter: cost and latency trade-offs vs. LLMs, and where they win in practice.Agentic patterns: from ReAct and Reflexion to Graph-of-Thoughts, voting councils, and self-consistency.Architectures: pipelines, hubs, and councils with robust hand-offs, retries, and deadlines.Infrastructure: serving SLMs efficiently with quantization, batching, and optimized runtimes.Memory and retrieval: vector databases, summarization strategies, and privacy-aware storage.Frameworks in action: LangGraph, AutoGen, CrewAI, DSPy, LlamaIndex, smolagents.Evaluation & observability: benchmarks, CI/CD test gates, dashboards, and regression alerts.Governance & safety: guardrails, auditability, and policies for production systems.Case studies & playbooks: real-world workflows in customer support, analytics, CI/CD, and enterprise RAG.Unlike books that focus only on frameworks or introductions to small models, this work is practical and production-oriented. It bridges the gap between theory and deployment, offering actionable patterns, design strategies, and reliability practices that today's AI engineers demand.Whether you are an AI engineer seeking efficiency, a researcher interested in orchestration, or a professional preparing for the shift from monolithic LLMs to collaborative SLM ecosystems, this book gives you the tools to build smarter, faster, and more efficient AI systems. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
