Название: The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science SolutionsАвтор: Marco Scutari, Mauro Malvestio
Издательство: CRC Press
Серия: Machine Learning & Pattern Recognition
Год: 2023
Страниц: 357
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
Machine Learning (ML) 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.
The book starts with a brief introduction to Machine Learning and software engineering, to set out how we view them and how we think that they should interact in practical applications. The remainder is structured in four parts, from foundational to practical:
1. Foundations of Scientific Computing: covering key topics that are foundational for the planning, analysis and design of Machine Learning software, such
as: the trade-offs of using different hardware configurations; the characteristics of different data types and of suitable data structures; and the analysis of algorithms to determine their computational complexity.
2. Best Practices for Machine Learning and Data Science: revisiting best practices in software engineering from the point of view of a Machine Learning engineer, from writing, troubleshooting and deploying code to production (that is, serving models) to writing technical documentation.
3. Tools and Technologies: discussing broad classes of tools that shape how we think about what is feasible to do with machine learning pipelines,
with examples from the state of the art and the trade-offs they make.
4. A Case Study: putting the recommendations in the previous chapters into practice by discussing and prototyping a machine learning pipeline for natural language understanding from the work of Lipizzi et al.
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.
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