You're reading from Machine Learning Infrastructure and Best Practices for Software Engineers Take your machine learning software from a prototype to a fully fledged software system

Product type Paperback

Published in Jan 2024

Publisher Packt

ISBN-13 9781837634064

Length 346 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Miroslaw Staron

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Table of Contents (24) Chapters

Preface

1. Part 1:Machine Learning Landscape in Software Engineering

2. Machine Learning Compared to Traditional Software FREE CHAPTER

3. Elements of a Machine Learning System

4. Data in Software Systems – Text, Images, Code, and Their Annotations

5. Data Acquisition, Data Quality, and Noise

6. Quantifying and Improving Data Properties

7. Part 2: Data Acquisition and Management

8. Processing Data in Machine Learning Systems

9. Feature Engineering for Numerical and Image Data

10. Feature Engineering for Natural Language Data

11. Part 3: Design and Development of ML Systems

12. Types of Machine Learning Systems – Feature-Based and Raw Data-Based (Deep Learning)

13. Training and Evaluating Classical Machine Learning Systems and Neural Networks

14. Training and Evaluation of Advanced ML Algorithms – GPT and Autoencoders

15. Designing Machine Learning Pipelines (MLOps) and Their Testing

16. Designing and Implementing Large-Scale, Robust ML Software

17. Part 4: Ethical Aspects of Data Management and ML System Development

18. Ethics in Data Acquisition and Management

19. Ethics in Machine Learning Systems

20. Integrating ML Systems in Ecosystems

21. Summary and Where to Go Next

22. Index

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Extracting data from product databases – GitHub and Git

JIRA and Gerrit are, to some extent, additional tools to the main product development tools. However, every software development organization uses a source code repository to store the main asset – the source code of the company’s software product. Today, the tools that are used the most are Git version control and its close relative, GitHub. Source code repositories can be a very useful source of data for machine learning systems – we can extract the source code of the product and analyze it.

GitHub is a great source of data for machine learning if we use it responsibly. Please remember that the source code provided as open source, by the community, is not for profiting off. We need to follow the licenses and we need to acknowledge the contributions that were made by the authors, contributors, and maintainers of the open source community. Regardless of the license, we are always able to analyze...

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You're reading from Machine Learning Infrastructure and Best Practices for Software Engineers Take your machine learning software from a prototype to a fully fledged software system

Table of Contents (24) Chapters

Extracting data from product databases – GitHub and Git

Authors (1)

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You're reading from Machine Learning Infrastructure and Best Practices for Software Engineers Take your machine learning software from a prototype to a fully fledged software system

Table of Contents (24) Chapters

Extracting data from product databases – GitHub and Git

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