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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

Abstract Syntax Tree (AST) 58

ACM

URL 277

advanced models

AEs 199

classical ML, migrating to 198

transformers 200, 201

advanced text processing

output visualization 55 - 57

AE 199

training and evaluation 210 - 217

AI engineering 242

AIMQ model

quality dimensions 77, 78

algorithms 23

data, preparing for 24

Amazon Web Services

URL 38

annotation 59, 60, 198

Area Under Curve/Receiver Operation Characteristics (AUROC) 15

attribute noise

eliminating 95 - 100

autoencoders 138 - 142

bag-of-words tokenizer 149 - 151

BERT model 172, 173

best practices 306 - 311

bias 280

bias, ML

algorithm bias 280

confirmation bias 280

measurement bias 280

measuring 281 - 285

metrics 285

monitoring 281 - 285

...

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