Reliable Machine Learning
Niall Richard Murphy, Todd Underwood, Kranti Parisa, Cathy Chen, D Sculley
Idioma: inglés
Editorial: O'Reilly Media, US, 2022
- Tapa blanda
- Nuevo
Librería: Rarewaves.com USA, London, London, Reino UnidoRarewaves.com USA
Vendedor de IberLibro desde 11 de junio de 2025
Condición: Nuevo
EUR 70,08
Cantidad disponible: 2 disponibles
Añadir al carritoDescripción del artículo del vendedor
Whether you're part of a small startup or a planet-spanning megacorp, this practical book shows data scientists, SREs, and business owners how to run ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization.By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guests show you how to run an efficient ML system. Whether you want to increase revenue, optimize decision-making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind.You'll examine:What ML is: how it functions and what it relies onConceptual frameworks for understanding how ML "loops" workEffective "productionization," and how it can be made easily monitorable, deployable, and operableWhy ML systems make production troubleshooting more difficult, and how to get around themHow ML, product, and production teams can communicate effectively.…
N° de ref. del artículo LU-9781098106225
- Título
- Reliable Machine Learning
- Autor
- Niall Richard Murphy, Todd Underwood, Kranti Parisa, Cathy Chen, D Sculley
- Editorial
- O'Reilly Media, US
- Año de publicación
- 2022
- Estado
- New
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 1098106229
- ISBN 13
- 9781098106225
Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. You'll gain insight into everything from how to do model monitoring in production to how to run a well-tuned model development team in a product organization.
By applying an SRE mindset to machine learning, authors and engineering professionals Cathy Chen, Kranti Parisa, Niall Richard Murphy, D. Sculley, Todd Underwood, and featured guest authors show you how to run an efficient and reliable ML system. Whether you want to increase revenue, optimize decision making, solve problems, or understand and influence customer behavior, you'll learn how to perform day-to-day ML tasks while keeping the bigger picture in mind.
You'll examine:- What ML is: how it functions and what it relies on
- Conceptual frameworks for understanding how ML "loops" work
- How effective productionization can make your ML systems easily monitorable, deployable, and operable
- Why ML systems make production troubleshooting more difficult, and how to compensate accordingly
- How ML, product, and production teams can communicate effectively
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Niall Murphy has worked in Internet infrastructure since the mid-1990s, specializing in large online services. He has worked with all of the major cloud providers from their Dublin, Ireland offices, and most recently at Microsoft, where he was global head of Azure Site Reliability Engineering (SRE). His first exposure to machine learning came with managing the Ads ML teams in Google's Dublin office and working with Todd Underwood in Pittsburgh, though it has continued to fascinate him since. He is the instigator, co-author, and editor of the two Google SRE books, and he is probably one of the few people in the world to hold degrees in Computer Science, Mathematics, and Poetry Studies. He lives in Dublin with his wife and two children, and works on a startup involving ML in the SRE space
Kranti K. Parisa is currently the Vice President & Head of Product Engineering at Dialpad. His teams build large scale, cloud native real-time business communications & collaboration software with industry leading in-house AI/ML & Telephony technology. Before Dialpad, he has led teams that are responsible for search and personalization platforms, products and services at Apple. Kranti was a cofounder, CTO and technical advisor of multiple start-ups focusing on cloud computing, SaaS, and enterprise search. He has contributed to the Apache Lucene/Solr community and co-authored the book Apache Solr Enterprise Search Server. For his outstanding contributions to Search & Discovery, U.S. Government has recognized him as a Person of Extraordinary Ability (EB1A).
D. Sculley is currently the CEO of Kaggle and GM of Third Party ML Ecosystems at Google, and previously has been a Director in the Google Brain Team and the lead of some of Google's most critical production machine learning pipelines. He has focused on issues of technical debt in machine learning, along with robustness and reliability of models and pipelines, and has led teams applying machine learning to problems as diverse as ad click through prediction and abuse prevention to protein design and scientific discovery. Additionally, he has helped to create Google's Machine Learning Crash Course, which has taught ML to millions of people worldwide.
Todd Underwood is a Senior Director at Google and leads Machine Learning SRE. He is also Site Lead for Google's Pittsburgh office. ML SRE teams build and scale internal and external ML services, and are critical to almost every significant product at Google. Before working at Google, Todd held a variety of roles at Renesys (in charge of operations, security, and peering for Internet intelligence services) now part of Oracle's Cloud, and before that he was Chief Technology Officer of Oso Grande, an independent Internet service provider in New Mexico.
“Acerca de” puede pertenecer a otra edición de este título.
Rarewaves.com USA
London, London, Reino Unido
Vendedor de IberLibro desde 11 de junio de 2025
Tarifas de envío de Reino Unido a Estados Unidos de America
| Artículo | De 7 a 12 días hábiles | De 7 a 12 días hábiles |
|---|---|---|
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