The Pragmatic Programmer for Machine Learning : Engineering Analytics and Data Science Solutions book cover
1st Edition

The Pragmatic Programmer for Machine Learning
Engineering Analytics and Data Science Solutions




  • Available for pre-order on March 10, 2023. Item will ship after March 31, 2023
ISBN 9780367263508
March 31, 2023 Forthcoming by Chapman & Hall
346 Pages 32 B/W Illustrations

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USD $149.95

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Book Description

Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life, yet software engineering has played a remarkably small role compared to other disciplines. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions addresses such a disparity.

Comprising a complete 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 learning practices can be integrated with the workflows of domain experts.

From choosing the right hardware to analysing algorithms and designing scalable architectures, this guide to software engineering will appeal to machine learning and data science specialists, while also utilising natural language and clear case studies to be accessible for students of computer science and aspiring programmers.

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Table of Contents

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

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Author(s)

Biography

Marco Scutari is a senior lecturer 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 software development consultant at Vipera, with more than
15 years of experience in software engineering and IT operations in consulting
and product companies as CTO. His research focuses on software engineering,
machine learning systems, embedded systems and cloud computing.