ML Diagnostics
ML Diagnostics refers to the processes and techniques used to identify, understand, and troubleshoot problems in machine learning models, data, and their performance throughout the model lifecycle.
What is ML Diagnostics?
ML Diagnostics involves analyzing a model's predictions, errors, training behavior, and input data to determine why it may not be performing as expected. It can include examining metrics, detecting overfitting or underfitting, analyzing misclassifications, checking data quality, and identifying issues such as data or model drift.
Why is ML Diagnostics Important?
ML Diagnostics helps teams understand model failures and improve reliability, accuracy, and performance. By identifying the underlying causes of problems rather than only measuring outcomes, teams can make better decisions about data, model architecture, training, and deployment.
Common use cases
ML Diagnostics is commonly used for model debugging, error analysis, performance monitoring, data quality checks, detecting drift, identifying bias, and troubleshooting production machine learning systems.