Machine Learning Infrastructure and Best Practices for Software Engineers
eBook - PDF

Machine Learning Infrastructure and Best Practices for Software Engineers

Take your machine learning software from a prototype to a fully fledged software system

  1. 346 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Machine Learning Infrastructure and Best Practices for Software Engineers

Take your machine learning software from a prototype to a fully fledged software system

About this book

No detailed description available for "Machine Learning Infrastructure and Best Practices for Software Engineers".

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Yes, you can access Machine Learning Infrastructure and Best Practices for Software Engineers by Miroslaw Staron in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Warehousing. We have over one million books available in our catalogue for you to explore.

Information

Publisher
De Gruyter
Year
2024
eBook ISBN
9781837636945
Edition
0
Topic
Computer Science
Subtopic
Data Warehousing
Index
Computer Science

Table of contents

  1. Cover
  2. Title page
  3. Copyright and credits
  4. Dedication
  5. Contributors
  6. Table of contents
  7. Preface
  8. Part 1: Machine Learning Landscape in Software Engineering
  9. Chapter 1: Machine Learning Compared to Traditional Software
  10. Chapter 2: Elements of a Machine Learning System
  11. Chapter 3: Data in Software Systems – Text, Images, Code, and Their Annotations
  12. Chapter 4: Data Acquisition, Data Quality, and Noise
  13. Chapter 5: Quantifying and Improving Data Properties
  14. Part 2: Data Acquisition and Management
  15. Chapter 6: Processing Data in Machine Learning Systems
  16. Chapter 7: Feature Engineering for Numerical and Image Data
  17. Chapter 8: Feature Engineering for Natural Language Data
  18. Part 3: Design and Development of ML Systems
  19. Chapter 9: Types of Machine Learning Systems – Feature-Based and Raw Data-Based (Deep Learning)
  20. Chapter 10: Training and Evaluating Classical Machine Learning Systems and Neural Networks
  21. Chapter 11: Training and Evaluation of Advanced ML Algorithms – GPT and Autoencoders
  22. Chapter 12: Designing Machine Learning Pipelines (MLOps) and Their Testing
  23. Chapter 13: Designing and Implementing Large-Scale, Robust ML Software
  24. Part 4: Ethical Aspects of Data Management and ML System Development
  25. Chapter 14: Ethics in Data Acquisition and Management
  26. Chapter 15: Ethics in Machine Learning Systems
  27. Chapter 16: Integrating ML Systems in Ecosystems
  28. Chapter 17: Summary and Where to Go Next
  29. Index
  30. Other Books You May Enjoy