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

To know where we’re going, we need to know where  we’ve been

My journey with computers started in the early 1990s, with Atari 800XL. Once I got my hands on that computer, I was amazed by the sheer fact that it could do what I told it to do. My first program was, of course, a program in BASIC:

10 PRINT Hello world!
20 GOTO 10

It’s neither well-formed nor a very useful program, but that was all I could do at the time. This first program shaped my entire career as it sparked my interest in software development. Later on, during my professional career, I realized that professional software engineering is much more than just writing source code and compiling it. The programs need to be well-formed, well-documented, well-designed, and well-tested (among many other things). This observation, in turn, shaped my view on software engineering as a discipline that can turn homebrewed software into a piece of art that can be used over long periods if well maintained...