BeautyBooksElectronicsHome & KitchenSports & OutdoorsIndustrial & ScientificToys & Games
Deliver toQatar
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

Details:

Visit the O'Reilly Media Store

Reliable Machine Learning: Applying SRE Principles to ML in Production

Product ID: U1098106229
|

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.

Every product in the BOLO catalogue is sourced through our Verified Global Supply Network of 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.

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 Qatar. Any items found to be restricted or prohibited for sale within the Qatar will be cancelled prior to shipment. We take proactive measures to ensure that only products permitted for sale in Qatar 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.

Similar suggestions by Bolo

Share with

Or share with link

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

Visit the O'Reilly Media Store

Reliable Machine Learning: Applying SRE Principles to ML in Production

Product ID: U1098106229
Reliable Machine Learning: Applying SRE Principles to ML in Production-0
|

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.

Every product in the BOLO catalogue is sourced through our Verified Global Supply Network of 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.

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 Qatar. Any items found to be restricted or prohibited for sale within the Qatar will be cancelled prior to shipment. We take proactive measures to ensure that only products permitted for sale in Qatar 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

Details:

Similar suggestions by Bolo

Share with

Or share with link

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