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Reliable Machine Learning: Applying SRE Principles to ML in Production

Description:

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
Read more

Editorial Reviews

Review

"A great model-agnostic deep dive into the product and technical aspects of ML systems. A guide every team should have for identifying and managing incidents when striving for reliability." - Goku Mohandas, Founder of Made With ML

"You've honed your machine learning expertise and are ready for your ideas to enter production — this treasure trove of tips from experienced practitioners will help ensure that journey is a smooth one, while also highlighting important ethical and organisational considerations." - David J. Groom

"Reliable Machine Learning is a must-read for people building real-world machine learning systems. It provides a blueprint for thinking about the complex and nuanced issues of developing machine learning enabled products." - Brian Spiering Data Science Instructor

"In a world where ML is becoming part of the default approach to problems, building a reliable and scalable solution is becoming a necessity. This book provides the groundwork for building an ML system that you can rely on." - James Blessing

"I don't care how much data science work you've done in the past, or how expert you are on the statistical foundations of Machine Learning. I don't care if you have read every line of the Tensorflow Source Code, or implemented your own distributed ML training from scratch. Before you ever put a real system based on Machine Learning into deployment you will benefit from reading this book. This is what is needed for the thousands of upcoming ML deployments where their usefulness is a double-edged sword. The more useful, the higher the stakes around safety, security, paying customers who are counting on you, fairness, or policy decisions that will be made on the basis of your system. This book thoroughly surveys the operations you need to be running if you have this level of responsibility, and you can rest assured that it comes from combined decades of hard won experience." - Andrew Moore, VP Google

From the Back Cover

Whether you're part of a small startup or a planet-spanning megacorp, this practical book shows data scientists, software engineers, SREs, product managers, 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

Reviews:

5.0 out of 5 stars Nice survey of what you'll need to think about when productionizing ML systems

S.L. · February 20, 2024

A nice in-breadth look at things you will need to consider when productionising machine learning systems.Minor nits: This book could have been 10% shorter if they had removed all "Foobar is not in the scope of this book" disclaimers (I do not recommend making a drinking game out of it). 15.3 also just kind of reads like an ad for Landing AI?

One of the rare resources of quality to implement MLOps the right way

D.J. · October 18, 2022

I rarely buy Oreilly books since I think tech books are too quickly outdated but "Reliable Machine Learning" is completely technology agnostic, it's all about best practices and the flow of thinking to design robust and reliable machine learning systems.I was happy to see it covers a large problem space: from designing robust data pipelines, ensure inference fairness and identifying data biases, model observability, scalable serving, to the organizational challenges to integrate such systems into a real company.

Visit the O'Reilly Media Store

Reliable Machine Learning: Applying SRE Principles to ML in Production

Product ID: U1098106229
Condition: New

4.4

AED28691

Price includes VAT & Import Duties
Type: Paperback
Availability: In Stock

Quantity:

|

Order today to get by 7-14 business days

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Imported From: United States

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While we strive to display accurate information, variations in packaging, labeling, instructions, or formulation may occasionally occur due to regional differences or supplier updates. For detailed or manufacturer-specific information, please contact the brand directly or reach out to BOLO Support for assistance.

Unless otherwise stated, all prices displayed on the product page include applicable taxes and import duties.

BOLO operates in accordance with the laws and regulations of United Arab Emirates. Any items found to be restricted or prohibited for sale within the UAE will be cancelled prior to shipment. We take proactive measures to ensure that only products permitted for sale in United Arab Emirates are listed on our website.

All items are shipped by air, and any products classified as “Dangerous Goods (DG)” under IATA regulations will be removed from the order and cancelled.

All orders are processed manually, and we make every effort to process them promptly once confirmed. Products cancelled due to the above reasons will be permanently removed from listings across the website.

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Visit the O'Reilly Media Store

Reliable Machine Learning: Applying SRE Principles to ML in Production

Product ID: U1098106229
Condition: New

4.4

Reliable Machine Learning: Applying SRE Principles to ML in Production-0
Type: Paperback

AED28691

Price includes VAT & Import Duties
Availability: In Stock

Quantity:

|

Order today to get by 7-14 business days

This item qualifies for free delivery

Returns & Warranty policies

Imported From: United States

At BOLO, we work hard to ensure the products you receive are new, genuine, and sourced from reputable suppliers.

BOLO is not an authorized or official retailer for most brands, nor are we affiliated with manufacturers unless specifically stated on a product page. Instead, we source verified sellers, authorized distributors or directly from the manufacturer.

Each product undergoes thorough inspection and verification at our consolidation and fulfilment centers to ensure it meets our strict authenticity and quality standards before being shipped and delivered to you.

If you ever have concerns regarding the authenticity of a product purchased from us, please contact Bolo Support. We will review your inquiry promptly and, if necessary, provide documentation verifying authenticity or offer a suitable resolution.

Your trust is our top priority, and we are committed to maintaining transparency and integrity in every transaction.

All product information, images, descriptions, and reviews originate from the manufacturer or from trusted sellers overseas. BOLO is not affiliated with, endorsed by, or an authorized retailer for most brands listed on our website unless stated otherwise.

While we strive to display accurate information, variations in packaging, labeling, instructions, or formulation may occasionally occur due to regional differences or supplier updates. For detailed or manufacturer-specific information, please contact the brand directly or reach out to BOLO Support for assistance.

Unless otherwise stated, all prices displayed on the product page include applicable taxes and import duties.

BOLO operates in accordance with the laws and regulations of United Arab Emirates. Any items found to be restricted or prohibited for sale within the UAE will be cancelled prior to shipment. We take proactive measures to ensure that only products permitted for sale in United Arab Emirates are listed on our website.

All items are shipped by air, and any products classified as “Dangerous Goods (DG)” under IATA regulations will be removed from the order and cancelled.

All orders are processed manually, and we make every effort to process them promptly once confirmed. Products cancelled due to the above reasons will be permanently removed from listings across the website.

Description:

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
Read more

Editorial Reviews

Review

"A great model-agnostic deep dive into the product and technical aspects of ML systems. A guide every team should have for identifying and managing incidents when striving for reliability." - Goku Mohandas, Founder of Made With ML

"You've honed your machine learning expertise and are ready for your ideas to enter production — this treasure trove of tips from experienced practitioners will help ensure that journey is a smooth one, while also highlighting important ethical and organisational considerations." - David J. Groom

"Reliable Machine Learning is a must-read for people building real-world machine learning systems. It provides a blueprint for thinking about the complex and nuanced issues of developing machine learning enabled products." - Brian Spiering Data Science Instructor

"In a world where ML is becoming part of the default approach to problems, building a reliable and scalable solution is becoming a necessity. This book provides the groundwork for building an ML system that you can rely on." - James Blessing

"I don't care how much data science work you've done in the past, or how expert you are on the statistical foundations of Machine Learning. I don't care if you have read every line of the Tensorflow Source Code, or implemented your own distributed ML training from scratch. Before you ever put a real system based on Machine Learning into deployment you will benefit from reading this book. This is what is needed for the thousands of upcoming ML deployments where their usefulness is a double-edged sword. The more useful, the higher the stakes around safety, security, paying customers who are counting on you, fairness, or policy decisions that will be made on the basis of your system. This book thoroughly surveys the operations you need to be running if you have this level of responsibility, and you can rest assured that it comes from combined decades of hard won experience." - Andrew Moore, VP Google

From the Back Cover

Whether you're part of a small startup or a planet-spanning megacorp, this practical book shows data scientists, software engineers, SREs, product managers, 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

Reviews:

5.0 out of 5 stars Nice survey of what you'll need to think about when productionizing ML systems

S.L. · February 20, 2024

A nice in-breadth look at things you will need to consider when productionising machine learning systems.Minor nits: This book could have been 10% shorter if they had removed all "Foobar is not in the scope of this book" disclaimers (I do not recommend making a drinking game out of it). 15.3 also just kind of reads like an ad for Landing AI?

One of the rare resources of quality to implement MLOps the right way

D.J. · October 18, 2022

I rarely buy Oreilly books since I think tech books are too quickly outdated but "Reliable Machine Learning" is completely technology agnostic, it's all about best practices and the flow of thinking to design robust and reliable machine learning systems.I was happy to see it covers a large problem space: from designing robust data pipelines, ensure inference fairness and identifying data biases, model observability, scalable serving, to the organizational challenges to integrate such systems into a real company.

Similar suggestions by Bolo

More from this brand

Similar items from “AI & Machine Learning”

Share with

Or share with link

https://www.bolo.ae/products/U1098106229