9798299481280 - multi-agent workflows with slms: smarter, faster, and more efficient ai systems di bernie, bernand (5 risultati)
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: Neu. Neuware - 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 com…es when SLMs are orchestrated together into multi-agent workflows.
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Da: California Books, Miami, FL, U.S.A.California Books
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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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
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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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.
