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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23. Other Books You May Enjoy

Extracting data from software engineering tools – Gerrit and Jira

To illustrate how to work with data extraction, let’s extract data from a popular software engineering tool for code reviews – Gerrit. This tool is used for reviewing and discussing fragments of code developed by individual programmers, just before they are integrated into the main code base of the product.

The following program code shows how to access the database of Gerrit – that is, through the JSON API – and how to extract the list of all changes for a specific project. This program uses the Python pygerrit2 package (https://pypi.org/project/pygerrit2/). This module helps us use the JSON API as it provides Python functions instead of just JSON strings:

# importing libraries
from pygerrit2 import GerritRestAPI
# A bit of config - repo
gerrit_url = "https://gerrit.onap.org/r"
# since we use a public OSS repository
auth = None
# this line gets sets the parameters for...

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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 software engineering tools – Gerrit and Jira

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 software engineering tools – Gerrit and Jira

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