Synopsis
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.
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About the Author
Cathy Chen, CPCC, MA specializes in coaching tech leaders enabling development of their own skills in leading teams. She has held the role of technical program manager, product manager, and engineering manager. She has led teams in large tech companies and startups launching product features, internal tools, and operating large systems. Cathy has a BS in Electrical Engineering from UC Berkeley & MA in Organizational Psychology from Teachers College at Columbia University. Currently, Cathy lives with her partner in Pittsburgh, PA and works at Google in SRE. 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.
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Paperback. Condition: New. 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. Seller Inventory # LU-9781098106225
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Paperback. Condition: new. Paperback. 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 on Conceptual frameworks for understanding how ML ""loops"" work Effective ""productionization,"" and how it can be made easily monitorable, deployable, and operable Why ML systems make production troubleshooting more difficult, and how to get around them How ML, product, and production teams can communicate effectively" 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781098106225
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