You're reading from Debugging Machine Learning Models with Python Develop high-performance, low-bias, and explainable machine learning and deep learning models

Product type Paperback

Published in Sep 2023

Publisher Packt

ISBN-13 9781800208582

Length 344 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Deep Learning

Author (1):

Ali Madani

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Table of Contents (26) Chapters

Preface

1. Part 1:Debugging for Machine Learning Modeling

2. Chapter 1: Beyond Code Debugging FREE CHAPTER

3. Chapter 2: Machine Learning Life Cycle

4. Chapter 3: Debugging toward Responsible AI

5. Part 2:Improving Machine Learning Models

6. Chapter 4: Detecting Performance and Efficiency Issues in Machine Learning Models

7. Chapter 5: Improving the Performance of Machine Learning Models

8. Chapter 6: Interpretability and Explainability in Machine Learning Modeling

9. Chapter 7: Decreasing Bias and Achieving Fairness

10. Part 3:Low-Bug Machine Learning Development and Deployment

11. Chapter 8: Controlling Risks Using Test-Driven Development

12. Chapter 9: Testing and Debugging for Production

13. Chapter 10: Versioning and Reproducible Machine Learning Modeling

14. Chapter 11: Avoiding and Detecting Data and Concept Drifts

15. Part 4:Deep Learning Modeling

16. Chapter 12: Going Beyond ML Debugging with Deep Learning

17. Chapter 13: Advanced Deep Learning Techniques

18. Chapter 14: Introduction to Recent Advancements in Machine Learning

19. Part 5:Advanced Topics in Model Debugging

20. Chapter 15: Correlation versus Causality

21. Chapter 16: Security and Privacy in Machine Learning

22. Chapter 17: Human-in-the-Loop Machine Learning

23. Assessments

24. Index

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25. Other Books You May Enjoy

Testing the code and the model

Although the performance of a machine learning model that is selected and brought to this stage of the life cycle can be further tested using one or multiple datasets, there are a series of tests that need to be done in this stage to make sure of this:

Ensuring the process of deployment and bringing the model into production goes smoothly
Ensuring the model will work as expected from a performance and computational cost perspective
Ensuring that using the model in production will not have legal and financial implications

Here are some such tests that can be used in this stage:

Unit tests: These are fast tests that make sure our code runs correctly. These tests are not specific to machine learning modeling and not even to this stage. Throughout the life cycle, you need to design unit tests to make sure your data processing and modeling code runs as expected.
A/B testing: This type of testing helps you, your team, and...

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You're reading from Debugging Machine Learning Models with Python Develop high-performance, low-bias, and explainable machine learning and deep learning models

Table of Contents (26) Chapters

Testing the code and the model

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You're reading from Debugging Machine Learning Models with Python Develop high-performance, low-bias, and explainable machine learning and deep learning models

Table of Contents (26) Chapters

Testing the code and the model

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