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

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Ecosystems

In the dynamic realm of software engineering, the tools, methodologies, and paradigms are in a constant state of evolution. Among the most influential forces driving this transformation is ML. While ML itself is a marvel of computational prowess, its true genius emerges when integrated into the broader software engineering ecosystems. This chapter delves into the nuances of embedding ML within an ecosystem. Ecosystems are groups of software that work together but are not connected at compile time. A well-known ecosystem is the PyTorch ecosystem, where a set of libraries work together in the context of ML. However, there is much more than that to ML ecosystems in software engineering.

From automated testing systems that learn from each iteration to recommendation engines that adapt to user behaviors, ML is redefining how software is designed, developed, and deployed. However, integrating ML into software engineering is not a mere plug-and-play operation. It demands a rethinking...