You're reading from Machine Learning Infrastructure and Best Practices for Software Engineers

Product type Book

Published in Jan 2024

Publisher Packt

ISBN-13 9781837634064

Pages 346 pages

Edition 1st Edition

Languages

Python

Concepts

Machine Learning

Author (1):

Miroslaw Staron

Table of Contents (24) Chapters

Preface

1. Part 1:Machine Learning Landscape in Software Engineering

2. Machine Learning Compared to Traditional Software

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

Why subscribe?

23. Other Books You May Enjoy

Natural language data in software engineering and the rise of GitHub Copilot

Programming has always been a mixture of science, engineering, and creativity. Creating new programs and being able to instruct computers to do something has always been something that was considered worth paying for – that’s how all programmers make their living. There have been attempts to automate programming and to support smaller tasks – for example, provide programmers with suggestions on how to use a specific function or library method.

Good programmers, however, can make programs that last and that are readable for others. They can also make reliable programs that work without maintenance for a long period. The best programmers are the ones who can solve very difficult tasks and follow the principles and best practices of software engineering.

In 2020, something happened – GitHub Copilot entered the stage and showed that automated tools, based on large language models...