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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. This book addresses such a disparity.
List 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
About the author
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.
Summary
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. This book addresses such a disparity.
