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

Current developments

At the time of writing this book, the Technology Innovation Institute (https://www.tii.ae/) has just released its largest model – Falcon 170B. It is the largest fully open source model that is similar to the GPT-3.5 model. It shows the current direction of the research in large language models.

Although GPT-4 exists, which is larger by a factor of 1,000, we can develop very good software with moderately large models such as GPT-3.5. This brings us to some of the current topics that we, as a community, need to discuss. One of them is the energy sustainability of these models. Falcon-170B requires 400 GB of RAM (eight times that of an Nvidia A100 GPU) to execute (according to Hugging Face). We do not know how much hardware the GPT-4 model needs. The amount of electricity that it takes and the resources that it uses must be on par with what we get as value from that model.

We also approach limits to the conventional computational power when it comes...