Model Debugging

Model debugging is a systematic process for identifying, diagnosing, and fixing issues in machine learning or AI models, such as poor performance, bias, or unexpected behavior. It involves analyzing model outputs, data inputs, and internal states to understand root causes of problems. This practice is essential for ensuring models are reliable, fair, and effective in production environments.

Also known as: ML Debugging, AI Debugging, Model Troubleshooting, Debugging ML Models, Model Error Analysis

Why learn Model Debugging?

Developers should learn model debugging to improve model accuracy, reduce errors, and address ethical concerns like bias, especially when deploying models in critical applications like healthcare or finance. It is crucial during model development, validation, and maintenance phases to troubleshoot issues like overfitting, data leakage, or adversarial attacks, ensuring robust and trustworthy AI systems.

Learning Resources

• docs: Debugging Machine Learning Models
• tutorial: Model Debugging Tutorial by TensorFlow
• course: Coursera: Debugging and Improving ML Models
• video: YouTube: Introduction to Model Debugging
• book: Book: Debugging Machine Learning Models