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.
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