Machine learning system design: with end-to-end examples

By:

Babushkin, Valerii

Contributor(s):

Kravchenko, Arseny

Material type: Text

TextPublication details: Shelter Island Manning Publications 2025Description: xx, 351 pISBN:

9781633438750

Subject(s):

DDC classification:

006.31 BAB

Summary: 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 (https://www.manning.com/books/machine-learning-system-design)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.31 BAB (Browse shelf(Opens below))	1	Available		009224

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

(https://www.manning.com/books/machine-learning-system-design)

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