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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.

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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Reliable Machine Learning: Applying SRE Principles to ML in Production

Product ID: K1098106229
Condition: New

BHD4218

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

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Imported From: United Kingdom

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.

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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 Bahrain. Any items found to be restricted or prohibited for sale within the Bahrain will be cancelled prior to shipment. We take proactive measures to ensure that only products permitted for sale in Bahrain 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: K1098106229
Condition: New
Reliable Machine Learning: Applying SRE Principles to ML in Production-0
Type: Paperback

BHD4218

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 Kingdom

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

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.

Details:

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More from this brand

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https://www.bolo.bh/products/K1098106229