Isbn: 9781098106225 - reliable machine learning: applying sre principles to ml in production (34 resultados)

Paperback. Condición: Very Good. no jacket (paperback). First Edition. This paperback edition of Reliable Machine Learning is in excellent condition, with no visible signs of wear or damage. The cover and pages are intact and clean, and there are no inscriptions or library markings. The book does not include a dust jacket, as it is a paperback. Kate Dullea (ilustrador).…

Condición: acceptable. This copy has heavy highlighting.

paperback. Condición: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Condición: New.

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Paperback or Softback. Condición: New. Reliable Machine Learning: Applying Sre Principles to ML in Production. Book.

Paperback. Condición: 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.…

Paperback. Condición: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: As New. Unread book in perfect condition.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: new.

Paperback. Condición: 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.…

Paperback. Condición: 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.…

Condición: New.

Paperback. Condición: New.

Condición: New. 2022. Paperback. . . . . .

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New. In English.

Condición: New. 2022. Paperback. . . . . . Books ship from the US and Ireland.

Condición: New.

Paperback. Condición: Brand New. 350 pages. 9.19x7.00x1.00 inches. In Stock.

Condición: NEW.

Condición: New.

Paperback. Condición: 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.…

Condición: New. &Uumlber den AutorCathy 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.

Taschenbuch. Condición: Neu. Neuware - 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.…

Paperback. Condición: 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…