Observability Large Language Models by Sharma Ankush (17 results)
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
Contact seller4-star sellerCondition: New
£ 37.26
Free ShippingShips within U.S.A.Quantity: Over 20 available
Condition: New.
- Softcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
Contact seller5-star sellerCondition: New
£ 57.82
Free ShippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
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: Rarewaves USA, OSWEGO, IL, U.S.A.Rarewaves USA
Contact seller5-star sellerCondition: New
£ 62.04
Free ShippingShips within U.S.A.Quantity: 2 available
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: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, GermanyRheinberg-Buch Andreas Meier eK
Contact seller5-star sellerCondition: New
£ 51.78
£ 19.65 shippingShips from Germany to U.S.A.Quantity: 1 available
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. 264 pp. Englisch.
- Softcover
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
£ 51.78
£ 19.65 shippingShips from Germany to U.S.A.Quantity: 1 available
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. 264 pp. Englisch.
- Softcover
Seller: Wegmann1855, Zwiesel, GermanyWegmann1855
Contact seller5-star sellerCondition: New
£ 51.78
£ 22.17 shippingShips from Germany to U.S.A.Quantity: 1 available
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.
- Softcover
Seller: Speedyhen, Hertfordshire, United KingdomSpeedyhen
Contact seller5-star sellerCondition: New
£ 41.26
£ 41.00 shippingShips from United Kingdom to U.S.A.Quantity: 3 available
Condition: NEW.
- Softcover
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.Rarewaves USA United
Contact seller5-star sellerCondition: New
£ 62.53
£ 36.91 shippingShips within U.S.A.Quantity: 2 available
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: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 62.18
£ 41.86 shippingShips from Germany to U.S.A.Quantity: 3 available
Condition: New.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 51.78
£ 53.42 shippingShips from Germany to U.S.A.Quantity: 2 available
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 f…oundations 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.
- Softcover
Seller: Rarewaves.com UK, London, United KingdomRarewaves.com UK
Contact seller5-star sellerCondition: New
£ 52.29
£ 65.00 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
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: Books-by-Floh, Paderborn, GermanyBooks-by-Floh
Contact seller4-star sellerCondition: New
£ 51.78
£ 89.71 shippingShips from Germany to U.S.A.Quantity: 2 available
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. 264 pp. Englisch.
- Softcover
- Print on Demand
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contact seller5-star sellerCondition: New
£ 37.25
Free ShippingShips within U.S.A.Quantity: 1 available
Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Softcover
- Print on Demand
Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
Contact seller5-star sellerCondition: New
£ 40.98
£ 4.70 shippingShips from Italy to U.S.A.Quantity: Over 20 available
Condition: new. Questo è un articolo print on demand.
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
£ 47.99
£ 37.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
£ 51.78
£ 51.26 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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 (LLM…s). The book begins by laying the groundwork with the foundations 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.Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg 264 pp. Englisch.
- Softcover
- Print on Demand
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contact seller5-star sellerCondition: New
£ 80.62
£ 27.31 shippingShips from Australia to U.S.A.Quantity: 1 available
Paperback. Condition: new. Paperback. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
