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Machine Learning Infrastructure and Best Practices for Software Engineers

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
Author (1):
Miroslaw Staron Miroslaw Staron
Profile icon 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 23. Other Books You May Enjoy

Part 2: Data Acquisition and Management

Machine learning software depends on data much more than other types of software. In order to make use of statistical learning, we need to collect, process, and prepare data for the development of machine learning models. The data needs to be representative of the problems that the software solves and the services it provides, not only during development but also during operations. In this part of the book, we focus on the data – how we can acquire it and how we make it useful for the training, testing, and deployment of machine learning models.

This part has the following chapters:

  • Chapter 6, Processing Data in Machine Learning Systems
  • Chapter 7, Feature Engineering for Numerical and Image Data
  • Chapter 8, Feature Engineering for Natural Language Data
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Machine Learning Infrastructure and Best Practices for Software Engineers
Published in: Jan 2024 Publisher: Packt ISBN-13: 9781837634064
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