Ankush Sharma (103 results)
- Softcover
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- Hardcover
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- Softcover
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- Softcover
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Application of Soft Computing Techniques in Mechanical Engineering
Patnaik, Amar (EDT); Kukshal, Vikas (EDT); Agarwal, Pankaj (EDT); Sharma, Ankush (EDT); Choudhary, Mahavir (EDT)
- Softcover
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Soft Computing in Materials Development and Its Sustainability in the Manufacturing Sector
Patnaik, Amar (EDT); Kukshal, Vikas (EDT); Agarwal, Pankaj (EDT); Sharma, Ankush (EDT); Choudhary, Mahavir (EDT)
Language: English
Published by CRC Press, 2024
Series: Edge AI in Future Computing, Book 7 of 9. Book 7 of 9 - Edge AI in Future Computing
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Application of Soft Computing Techniques in Mechanical Engineering
Patnaik, Amar (EDT); Kukshal, Vikas (EDT); Agarwal, Pankaj (EDT); Sharma, Ankush (EDT); Choudhary, Mahavir (EDT)
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Paperback. Condition: New. This book is a comprehensive guide designed to equip engineers, data scientists, and AI practitioners with the principles, tools, and strategies needed to ensure reliability, performance, and accountability in Large Language Models (LLMs). The book begins by laying the groundwork with the foundations o…f observability, introducing LLMs, their significance in modern AI, and the critical role observability plays in maintaining robust systems. It then explores SRE principles, service level objectives, and incident response, while distinguishing the unique observability challenges that arise in AI and ML systems. Building on this foundation, the book dives into measuring performance, from defining SLOs tailored for LLMs to monitoring computational and token-level metrics. Readers gain practical insights into structured logging, debugging, and distributed tracing methods that provide visibility into complex LLM workflows. Scaling challenges are addressed through strategies for cross-model observability, autoscaling, latency reduction, and fault-tolerant infrastructure design. The book further explores chaos engineering, guiding readers through resilience testing in LLMs and the automation of chaos experiments in CI/CD pipelines. Finally, it highlights monitoring, retraining, and ethical considerations in AI observability, including governance, privacy, and accountability.In conclusion, this book provides a holistic roadmap to building reliable, transparent, and future-ready LLM systems.What you will learn:How to design observability pipelines for LLMs, including token-level logging, prompt tracing, and latency analysis.Techniques for applying chaos engineering principles to test LLM robustness under stress andfailure scenarios.Methods for building SLOs, SLAs, and dashboards tailored to inference quality and modelreliability.Strategies for monitoring hallucinations, drift, bias, and ethical failures in real-time.Who this book is for:This book is for AI infrastructure engineers, SREs, machine learning platform teams, and applied AI practitioners deploying or maintaining LLM-based applications.
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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Soft Computing in Materials Development and Its Sustainability in the Manufacturing Sector
Patnaik, Amar (EDT); Kukshal, Vikas (EDT); Agarwal, Pankaj (EDT); Sharma, Ankush (EDT); Choudhary, Mahavir (EDT)
Language: English
Published by CRC Press, 2024
Series: Edge AI in Future Computing, Book 7 of 9. Book 7 of 9 - Edge AI in Future Computing
- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
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Paperback. Condition: New. This book is a comprehensive guide designed to equip engineers, data scientists, and AI practitioners with the principles, tools, and strategies needed to ensure reliability, performance, and accountability in Large Language Models (LLMs). The book begins by laying the groundwork with the foundations o…f observability, introducing LLMs, their significance in modern AI, and the critical role observability plays in maintaining robust systems. It then explores SRE principles, service level objectives, and incident response, while distinguishing the unique observability challenges that arise in AI and ML systems. Building on this foundation, the book dives into measuring performance, from defining SLOs tailored for LLMs to monitoring computational and token-level metrics. Readers gain practical insights into structured logging, debugging, and distributed tracing methods that provide visibility into complex LLM workflows. Scaling challenges are addressed through strategies for cross-model observability, autoscaling, latency reduction, and fault-tolerant infrastructure design. The book further explores chaos engineering, guiding readers through resilience testing in LLMs and the automation of chaos experiments in CI/CD pipelines. Finally, it highlights monitoring, retraining, and ethical considerations in AI observability, including governance, privacy, and accountability.In conclusion, this book provides a holistic roadmap to building reliable, transparent, and future-ready LLM systems.What you will learn:How to design observability pipelines for LLMs, including token-level logging, prompt tracing, and latency analysis.Techniques for applying chaos engineering principles to test LLM robustness under stress andfailure scenarios.Methods for building SLOs, SLAs, and dashboards tailored to inference quality and modelreliability.Strategies for monitoring hallucinations, drift, bias, and ethical failures in real-time.Who this book is for:This book is for AI infrastructure engineers, SREs, machine learning platform teams, and applied AI practitioners deploying or maintaining LLM-based applications.
- Softcover
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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US$ 73.58
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Condition: New. pages cm.
Language: English
Published by CRC Press, 2024
Series: Edge AI in Future Computing, Book 7 of 9. Book 7 of 9 - Edge AI in Future Computing
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
Application of Soft Computing Techniques in Mechanical Engineering
Patnaik, Amar (EDT); Kukshal, Vikas (EDT); Agarwal, Pankaj (EDT); Sharma, Ankush (EDT); Choudhary, Mahavir (EDT)
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Condition: New.
Language: English
Published by CRC Press, 2024
Series: Edge AI in Future Computing, Book 7 of 9. Book 7 of 9 - Edge AI in Future Computing
- Softcover
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Soft Computing in Materials Development and Its Sustainability in the Manufacturing Sector
Patnaik, Amar (EDT); Kukshal, Vikas (EDT); Agarwal, Pankaj (EDT); Sharma, Ankush (EDT); Choudhary, Mahavir (EDT)
Language: English
Published by CRC Press, 2024
Series: Edge AI in Future Computing, Book 7 of 9. Book 7 of 9 - Edge AI in Future Computing
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condition: New. pages cm First edition Includes bibliographical references and index.
- Softcover
Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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Application of Soft Computing Techniques in Mechanical Engineering
Patnaik, Amar (EDT); Kukshal, Vikas (EDT); Agarwal, Pankaj (EDT); Sharma, Ankush (EDT); Choudhary, Mahavir (EDT)
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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- Softcover
Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Condition: New. In.
- Softcover
Seller: Wegmann1855, Zwiesel, GermanyWegmann1855
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Taschenbuch. Condition: Neu. Neuware -This book is a comprehensive guide designed to equip engineers, data scientists, and AI practitioners with the principles, tools, and strategies needed to ensure reliability, performance, and accountability in Large Language Models (LLMs). The book begins by laying the groundwork with the fo…undations of observability, introducing LLMs, their significance in modern AI, and the critical role observability plays in maintaining robust systems. It then explores SRE principles, service level objectives, and incident response, while distinguishing the unique observability challenges that arise in AI and ML systems. Building on this foundation, the book dives into measuring performance, from defining SLOs tailored for LLMs to monitoring computational and token-level metrics. Readers gain practical insights into structured logging, debugging, and distributed tracing methods that provide visibility into complex LLM workflows. Scaling challenges are addressed through strategies for cross-model observability, autoscaling, latency reduction, and fault-tolerant infrastructure design. The book further explores chaos engineering, guiding readers through resilience testing in LLMs and the automation of chaos experiments in CI/CD pipelines. Finally, it highlights monitoring, retraining, and ethical considerations in AI observability, including governance, privacy, and accountability.In conclusion, this book provides a holistic roadmap to building reliable, transparent, and future-ready LLM systems.What you will learn:- How to design observability pipelines for LLMs, including token-level logging, prompt tracing, and latency analysis.- Techniques for applying chaos engineering principles to test LLM robustness under stress andfailure scenarios.- Methods for building SLOs, SLAs, and dashboards tailored to inference quality and modelreliability.- Strategies for monitoring hallucinations, drift, bias, and ethical failures in real-time.Who this book is for:This book is for AI infrastructure engineers, SREs, machine learning platform teams, and applied AI practitioners deploying or maintaining LLM-based applications.
Language: English
Published by Taylor & Francis Ltd, 2024
Series: Edge AI in Future Computing, Book 7 of 9. Book 7 of 9 - Edge AI in Future Computing
- Softcover
Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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