Description:
Editorial Reviews
From the Back Cover
From the back cover:
In Machine Learning System Design: With end-to-end examples you'll find a step-by-step framework for creating, implementing, releasing, and maintaining your ML system. Every part of the life cycle is covered, from information gathering to keeping your system well-serviced. Each stage includes its own handy checklist of requirements and is fully illustrated with real-world examples, including interesting anecdotes from the author's own careers.
You'll follow two example companies each building a new ML system, exploring how their needs are expressed in design documents and learning best practices by writing your own. Along the way, you'll learn how to ace ML system design interviews, even at highly competitive FAANG-like companies, and improve existing ML systems by identifying bottlenecks and optimizing system performance.About the reader:
For readers who know the basics of both software engineering and machine learning. Examples in Python.
About the Author
Arseny Kravchenko is a seasoned ML engineer with a proven track record of building and optimizing reliable ML systems for startups, including real-time video processing, manufacturing optimization, and financial transactions analysis.
Reviews:
5.0 out of 5 stars For Experienced Engineers Transitioning To ML/GenAI Production Systems
(function() { P.when('cr-A', 'ready').execute(function(A) { if(typeof A.toggleExpanderAriaLabel === 'function') { A.toggleExpanderAriaLabel('review_text_read_more', 'Read more of this review', 'Read less of this review'); } }); })(); .review-text-read-more-expander:focus-visible { outline: 2px solid #2162a1; outline-offset: 2px; border-radius: 5px; } I picked this from a from an YT channel recommendation and was blown away! This book stands out from all others by being incredibly practical and very hands on.This is very much unlike other system design books as this book is bursting with easily digestible, hard earned insights, and painful lessons from failed deployments of business-critical, AI systems.If you have industry experience you will notice that all other books on ML system spend far too much time on basics and don’t contain any real practical insights; Too academic and not practical. While the basics are important there is a big experience gap that I see daily for the applied AI field. This book is the only resource that I have found that breaks this trend (outside of blogs).Unfortunately, if you are new to the industry, you might easily gloss over the insane amount of insights spoon fed to you here. I could be wrong, but keep that in mind. I could see this book helping you with ml system design interviews however.On the other hand, I strongly recommend this book for experienced swe’s that have transitioned to ml or even gen ai; you WILL get a lot out of this book!This can also double as an amazing desk reference, can’t recommend it enough!
4.0 out of 5 stars From Model to Production: A Practical Guide to ML System Design
A well-structured & practical guide to building and deploying ML systems, covering the entire ML lifecycle, from problem definition to deployment and monitoring. A must-read for ML engineers and data scientists looking to bridge the gap between research and real-world applications.
Visit the Valerii Babushkin (Author), Arseny Kravchenko (Author) Store
Machine Learning System Design: With end-to-end examples
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Visit the Valerii Babushkin (Author), Arseny Kravchenko (Author) Store
Machine Learning System Design: With end-to-end examples
AED34293
Quantity:
Order today to get by 7-14 business days
This item qualifies for free delivery
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:
Editorial Reviews
From the Back Cover
From the back cover:
In Machine Learning System Design: With end-to-end examples you'll find a step-by-step framework for creating, implementing, releasing, and maintaining your ML system. Every part of the life cycle is covered, from information gathering to keeping your system well-serviced. Each stage includes its own handy checklist of requirements and is fully illustrated with real-world examples, including interesting anecdotes from the author's own careers.
You'll follow two example companies each building a new ML system, exploring how their needs are expressed in design documents and learning best practices by writing your own. Along the way, you'll learn how to ace ML system design interviews, even at highly competitive FAANG-like companies, and improve existing ML systems by identifying bottlenecks and optimizing system performance.About the reader:
For readers who know the basics of both software engineering and machine learning. Examples in Python.
About the Author
Arseny Kravchenko is a seasoned ML engineer with a proven track record of building and optimizing reliable ML systems for startups, including real-time video processing, manufacturing optimization, and financial transactions analysis.
Reviews:
5.0 out of 5 stars For Experienced Engineers Transitioning To ML/GenAI Production Systems
(function() { P.when('cr-A', 'ready').execute(function(A) { if(typeof A.toggleExpanderAriaLabel === 'function') { A.toggleExpanderAriaLabel('review_text_read_more', 'Read more of this review', 'Read less of this review'); } }); })(); .review-text-read-more-expander:focus-visible { outline: 2px solid #2162a1; outline-offset: 2px; border-radius: 5px; } I picked this from a from an YT channel recommendation and was blown away! This book stands out from all others by being incredibly practical and very hands on.This is very much unlike other system design books as this book is bursting with easily digestible, hard earned insights, and painful lessons from failed deployments of business-critical, AI systems.If you have industry experience you will notice that all other books on ML system spend far too much time on basics and don’t contain any real practical insights; Too academic and not practical. While the basics are important there is a big experience gap that I see daily for the applied AI field. This book is the only resource that I have found that breaks this trend (outside of blogs).Unfortunately, if you are new to the industry, you might easily gloss over the insane amount of insights spoon fed to you here. I could be wrong, but keep that in mind. I could see this book helping you with ml system design interviews however.On the other hand, I strongly recommend this book for experienced swe’s that have transitioned to ml or even gen ai; you WILL get a lot out of this book!This can also double as an amazing desk reference, can’t recommend it enough!
4.0 out of 5 stars From Model to Production: A Practical Guide to ML System Design
A well-structured & practical guide to building and deploying ML systems, covering the entire ML lifecycle, from problem definition to deployment and monitoring. A must-read for ML engineers and data scientists looking to bridge the gap between research and real-world applications.
Similar suggestions by Bolo
More from this brand
Similar items from “Data Modeling & Design”
Share with
Or share with link
https://www.bolo.ae/products/U1633438759