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

Processing Data in Machine Learning Systems

We talked about data in Chapter 3, where we introduced the types of data that are used in machine learning systems. In this chapter, we’ll dive deeper into ways in which data and algorithms are entangled. We’ll talk about data in generic terms, but in this chapter, we’ll explain what kind of data is needed in machine learning systems. I’ll explain the fact that all kinds of data are used in numerical form – either as a feature vector or as more complex feature matrices. Then, I’ll explain the need to transform unstructured data (for example, text) into structured data. This chapter will lay the foundations for diving deeper into each type of data, which is the content of the next few chapters.

In this chapter, we will do the following:

Discuss the process of measurement (obtaining numerical data) and the measurement instruments that are used in that process
Visualize numerical data...

The rest of the chapter is locked

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

Processing Data in Machine Learning Systems

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

Processing Data in Machine Learning Systems

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