Name: Debugging Machine Learning Models with Python
Author: Ali Madani Stephen MacKinnon
ISBN: 9781800201132

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Debugging Machine Learning Models with Python

Ali Madani Stephen MacKinnon

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Dedication
Foreword
Contributors
Preface
Part 1 Debugging for Machine Learning Modeling
1 Beyond Code Debugging
Technical requirements
Machine learning at a glance
Types of machine learning modeling
Debugging in software development
Flaws in data used for modeling
Model and prediction-centric debugging
Summary
Questions
References
2 Machine Learning Life Cycle
Technical requirements
Before we start modeling
Data collection
Data selection
Data exploration
Data wrangling
Modeling data preparation
Model training and evaluation
Testing the code and the model
Model deployment and monitoring
Summary
Questions
References
3 Debugging toward Responsible AI
Technical requirements
Impartial modeling fairness in machine learning
Security and privacy in machine learning
Transparency in machine learning modeling
Accountable and open to inspection modeling
Data and model governance
Summary
Questions
References
Part 2 Improving Machine Learning Models
4 Detecting Performance and Efficiency Issues in Machine Learning Models
Technical requirements
Performance and error assessment measures
Visualization for performance assessment
Bias and variance diagnosis
Model validation strategy
Error analysis
Beyond performance
Summary
Questions
References
5 Improving the Performance of Machine Learning Models
Technical requirements
Options for improving model performance
Synthetic data generation
Improving pre-training data processing
Regularization to improve model generalizability
Summary
Questions
References
6 Interpretability and Explainability in Machine Learning Modeling
Technical requirements
Interpretable versus black-box machine learning
Explainability methods in machine learning
Practicing machine learning explainability in Python
Reviewing why having explainability is not enough
Summary
Questions
References
7 Decreasing Bias and Achieving Fairness
Technical requirements
Fairness in machine learning modeling
Sources of bias
Using explainability techniques
Fairness assessment and improvement in Python
Summary
Questions
References
Part 3 Low-Bug Machine Learning Development and Deployment
8 Controlling Risks Using Test-Driven Development
Technical requirements
Test-driven development for machine learning modeling
Machine learning differential testing
Tracking machine learning experiments
Summary
Questions
References
9 Testing and Debugging for Production
Technical requirements
Infrastructure testing
Integration testing of machine learning pipelines
Monitoring and validating live performance
Model assertion
Summary
Questions
References
10 Versioning and Reproducible Machine Learning Modeling
Technical requirements
Reproducibility in machine learning
Data versioning
Model versioning
Summary
Questions
References
11 Avoiding and Detecting Data and Concept Drifts
Technical requirements
Avoiding drifts in your models
Detecting drifts
Summary
Questions
References
Part 4 Deep Learning Modeling
12 Going Beyond ML Debugging with Deep Learning
Technical requirements
Introduction to artificial neural networks
Frameworks for neural network modeling
Summary
Questions
References
13 Advanced Deep Learning Techniques
Technical requirements
Types of neural networks
Convolutional neural networks for image shape data
Transformers for language modeling
Modeling graphs using deep neural networks
Summary
Questions
References
14 Introduction to Recent Advancements in Machine Learning
Technical requirements
Generative modeling
Reinforcement learning
Self-supervised learning (SSL)
Summary
Questions
References
Part 5 Advanced Topics in Model Debugging
15 Correlation versus Causality
Technical requirements
Correlation as part of machine learning models
Causal modeling to reduce risks and improve performance
Assessing causation in machine learning models
Causal modeling using Python
Summary
Questions
References
16 Security and Privacy in Machine Learning
Technical requirements
Encryption techniques and their use in machine learning
Homomorphic encryption
Differential privacy
Federated learning
Summary
Questions
References
17 Human-in-the-Loop Machine Learning
Humans in the machine learning life cycle
Human-in-the-loop modeling
Summary
Questions
References
Assessments
Chapter 1 – Beyond Code Debugging
Chapter 2 – Machine Learning Life Cycle
Chapter 3 – Debugging toward Responsible AI
Chapter 4 – Detecting Performance and Efficiency Issues in Machine Learning Models
Chapter 5 – Improving the Performance of Machine Learning Models
Chapter 6 – Interpretability and Explainability in Machine Learning Modeling
Chapter 7 – Decreasing Bias and Achieving Fairness
Chapter 8 – Controlling Risks Using Test-Driven Development
Chapter 9 – Testing and Debugging for Production
Chapter 10 – Versioning and Reproducible Machine Learning Modeling
Chapter 11 – Avoiding and Detecting Data and Concept Drifts
Chapter 12 – Going Beyond ML Debugging with Deep Learning
Chapter 13 – Advanced Deep Learning Techniques
Chapter 14 – Introduction to Recent Advancements in Machine Learning
Chapter 15 – Correlation versus Causality
Chapter 16 – Security and Privacy in Machine Learning
Chapter 17 – Human-in-the-Loop Machine Learning
Index
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Debugging Machine Learning Models with Python