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

Machine learning has revolutionized data work and is essential for everyday life. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions discusses how modern software engineering practices can be adapted to and integrated with the workflows of domain experts and probabilistic models. It provides a multi-disciplinary view of how traditional software engineering can be adapted to machine learning and data science, appealing to machine learning and data science specialists and students of computer science.

Format: Hardback
Length: 340 pages
Publication date: 31 March 2023
Publisher: Taylor & Francis Ltd

Machine learning has revolutionized the way we work with data, becoming an essential component of our daily lives. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions delves into how modern software engineering practices are integral to this transformative shift, both conceptually and in practical applications. This comprehensive book offers a multi-disciplinary perspective on how traditional software engineering can be adapted to and integrated with the workflows of domain experts and probabilistic models. It provides a thorough overview of designing machine learning pipelines, along with the state-of-the-art tools used to create them. From selecting the appropriate hardware to architecting efficient pipeline architectures and adopting best practices in software development, this guide appeals to machine learning and data science specialists, as well as providing accessible high-level principles for computer science students and aspiring programmers.

Machine learning has revolutionized the way we work with data, becoming an essential component of our daily lives. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions delves into how modern software engineering practices are integral to this transformative shift, both conceptually and in practical applications. This comprehensive book offers a multi-disciplinary perspective on how traditional software engineering can be adapted to and integrated with the workflows of domain experts and probabilistic models. It provides a thorough overview of designing machine learning pipelines, along with the state-of-the-art tools used to create them. From selecting the appropriate hardware to architecting efficient pipeline architectures and adopting best practices in software development, this guide appeals to machine learning and data science specialists, as well as providing accessible high-level principles for computer science students and aspiring programmers.

Weight: 820g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367263508