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

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.

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 Ph.D. 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 system and cloud computing.