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

Data Acquisition, Data Quality, and Noise

Data for machine learning systems can come directly from humans and software systems – usually called source systems. Where the data comes from has implications regarding what it looks like, what kind of quality it has, and how to process it.

The data that originates from humans is usually noisier than data that originates from software systems. We, as humans, are known for small inconsistencies and we can also understand things inconsistently. For example, the same defect reported by two different people could have a very different description; the same is true for requirements, designs, and source code.

The data that originates from software systems is often more consistent and contains less noise or the noise in the data is more regular than the noise in the human-generated data. This data is generated by source systems. Therefore, controlling and monitoring the quality of the data that’s generated automatically is different...

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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

Data Acquisition, Data Quality, and Noise

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Table of Contents (24) Chapters

Data Acquisition, Data Quality, and Noise

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