The Pragmatic Programmer for Machine Learning
Engineering Analytics and Data Science Solutions
1st Edition
Published on 31. March 2023
356 pages
978-1-000-85246-2 (ISBN)
Description
Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions discusses how modern software engineering practices are part of this revolution both conceptually and in practical applictions.
Comprising a broad overview of how to design machine learning pipelines as well as the state-of-the-art tools we use to make them, this book provides a multi-disciplinary view of how traditional software engineering can be adapted to and integrated with the workflows of domain experts and probabilistic models.
From choosing the right hardware to designing effective pipelines architectures and adopting software development best practices, this guide will appeal to machine learning and data science specialists, whilst also laying out key high-level principlesin a way that is approachable for students of computer science and aspiring programmers.
Comprising a broad overview of how to design machine learning pipelines as well as the state-of-the-art tools we use to make them, this book provides a multi-disciplinary view of how traditional software engineering can be adapted to and integrated with the workflows of domain experts and probabilistic models.
From choosing the right hardware to designing effective pipelines architectures and adopting software development best practices, this guide will appeal to machine learning and data science specialists, whilst also laying out key high-level principlesin a way that is approachable for students of computer science and aspiring programmers.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Illustrations
3 Tables, black and white; 32 Line drawings, black and white; 32 Illustrations, black and white
ISBN-13
978-1-000-85246-2 (9781000852462)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Marco Scutari | Mauro Malvestio
The Pragmatic Programmer for Machine Learning
Engineering Analytics and Data Science Solutions
Book
04/2025
1st Edition
Chapman & Hall/CRC
€69.90
Shipment within 15-20 days
Marco Scutari | Mauro Malvestio
The Pragmatic Programmer for Machine Learning
Engineering Analytics and Data Science Solutions
Book
03/2023
1st Edition
Chapman & Hall/CRC
€111.60
Shipment within 15-20 days
Persons
Marco Scutari is a Senior Researcher at Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA), Switzerland. He has held positions in statistics, statistical genetics and machine learning in the UK and Switzerland since completing his PhD in statistics in 2011. His research focuses on the theory of Bayesian networks and their applications to biological and clinical data, as well as statistical computing and software engineering.
Mauro Malvestio is a senior technologist based in Milan, Italy, with more than 15 years of experience in software engineering and IT operations in consulting and product companies as a CTO. His research focuses on software engineering, machine learning systems, embedded systems and cloud computing.
Mauro Malvestio is a senior technologist based in Milan, Italy, with more than 15 years of experience in software engineering and IT operations in consulting and product companies as a CTO. His research focuses on software engineering, machine learning systems, embedded systems and cloud computing.
Content
Preface
1 What is This Book About?
2 Hardware Architectures
3 Variable Types and Data Structures
4 Analysis of Algorithms
5 Designing and Structuring Pipelines
6 Writing Machine Learning Code
7 Packaging and Deploying Pipelines
8 Documenting Pipelines
9 Troubleshooting and Testing Pipelines
10 Tools for Developing Pipelines
11 Tools to Manage Pipelines in Production
12 Recommending Recommendations: A Recommender
System Using Natural Language Understanding
Bibliography
Index
1 What is This Book About?
2 Hardware Architectures
3 Variable Types and Data Structures
4 Analysis of Algorithms
5 Designing and Structuring Pipelines
6 Writing Machine Learning Code
7 Packaging and Deploying Pipelines
8 Documenting Pipelines
9 Troubleshooting and Testing Pipelines
10 Tools for Developing Pipelines
11 Tools to Manage Pipelines in Production
12 Recommending Recommendations: A Recommender
System Using Natural Language Understanding
Bibliography
Index
