Description:
From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside, you’ll find a reliable framework for building, maintaining, and improving machine learning systems at any scale or complexity.
In Machine Learning System Design: With end-to-end examples you will learn:
- The big picture of machine learning system design
- Analyzing a problem space to identify the optimal ML solution
- Ace ML system design interviews
- Selecting appropriate metrics and evaluation criteria
- Prioritizing tasks at different stages of ML system design
- Solving dataset-related problems with data gathering, error analysis, and feature engineering
- Recognizing common pitfalls in ML system development
- Designing ML systems to be lean, maintainable, and extensible over time
Authors Valeri Babushkin and Arseny Kravchenko have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You’ll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system.
About the technology
Designing and delivering a machine learning system is an intricate multistep process that requires many skills and roles. Whether you’re an engineer adding machine learning to an existing application or designing a ML system from the ground up, you need to navigate massive datasets and streams, lock down testing and deployment requirements, and master the unique complexities of putting ML models into production. That’s where this book comes in.
About the book
Machine Learning System Design shows you how to design and deploy a machine learning project from start to finish. You’ll follow a step-by-step framework for designing, implementing, releasing, and maintaining ML systems. As you go, requirement checklists and real-world examples help you prepare to deliver and optimize your own ML systems. You’ll especially love the campfire stories and personal tips, and ML system design interview tips.
What's inside
- Metrics and evaluation criteria
- Solve common dataset problems
- Common pitfalls in ML system development
- ML system design interview tips
About the reader
For readers who know the basics of software engineering and machine learning. Examples in Python.
About the author
Valerii Babushkin is an accomplished data science leader with extensive experience. He currently serves as a Senior Principal at BP. Arseny Kravchenko is a seasoned ML engineer currently working as a Senior Staff Machine Learning Engineer at Instrumental.
Table of Contents
Part 1
1 Essentials of machine learning system design
2 Is there a problem?
3 Preliminary research
4 Design document
Part 2
5 Loss functions and metrics
6 Gathering datasets
7 Validation schemas
8 Baseline solution
Part 3
9 Error analysis
10 Training pipelines
11 Features and feature engineering
12 Measuring and reporting results
Part 4
13 Integration
14 Monitoring and reliability
15 Serving and inference optimization
16 Ownership and maintenance
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.
Details:
Visit the Manning Publications Store
Machine Learning System Design: With end-to-end examples
QAR44583
Quantity:
Order today to get by 7-14 business days
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Visit the Manning Publications Store
Machine Learning System Design: With end-to-end examples
QAR44583
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.
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:
From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside, you’ll find a reliable framework for building, maintaining, and improving machine learning systems at any scale or complexity.
In Machine Learning System Design: With end-to-end examples you will learn:
- The big picture of machine learning system design
- Analyzing a problem space to identify the optimal ML solution
- Ace ML system design interviews
- Selecting appropriate metrics and evaluation criteria
- Prioritizing tasks at different stages of ML system design
- Solving dataset-related problems with data gathering, error analysis, and feature engineering
- Recognizing common pitfalls in ML system development
- Designing ML systems to be lean, maintainable, and extensible over time
Authors Valeri Babushkin and Arseny Kravchenko have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You’ll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system.
About the technology
Designing and delivering a machine learning system is an intricate multistep process that requires many skills and roles. Whether you’re an engineer adding machine learning to an existing application or designing a ML system from the ground up, you need to navigate massive datasets and streams, lock down testing and deployment requirements, and master the unique complexities of putting ML models into production. That’s where this book comes in.
About the book
Machine Learning System Design shows you how to design and deploy a machine learning project from start to finish. You’ll follow a step-by-step framework for designing, implementing, releasing, and maintaining ML systems. As you go, requirement checklists and real-world examples help you prepare to deliver and optimize your own ML systems. You’ll especially love the campfire stories and personal tips, and ML system design interview tips.
What's inside
- Metrics and evaluation criteria
- Solve common dataset problems
- Common pitfalls in ML system development
- ML system design interview tips
About the reader
For readers who know the basics of software engineering and machine learning. Examples in Python.
About the author
Valerii Babushkin is an accomplished data science leader with extensive experience. He currently serves as a Senior Principal at BP. Arseny Kravchenko is a seasoned ML engineer currently working as a Senior Staff Machine Learning Engineer at Instrumental.
Table of Contents
Part 1
1 Essentials of machine learning system design
2 Is there a problem?
3 Preliminary research
4 Design document
Part 2
5 Loss functions and metrics
6 Gathering datasets
7 Validation schemas
8 Baseline solution
Part 3
9 Error analysis
10 Training pipelines
11 Features and feature engineering
12 Measuring and reporting results
Part 4
13 Integration
14 Monitoring and reliability
15 Serving and inference optimization
16 Ownership and maintenance
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.
Details:
Similar suggestions by Bolo
More from this brand
Similar items from “Data Modeling & Design”
Share with
Or share with link
https://www.bolo.qa/products/U1633438759