- NLP Inputs: Dive into our guide on using Fiddler to monitor NLP models. Learn how a multi-class classifier is applied to the dataset and monitored with Vector Monitoring.
- Class Imbalance: Discover how Fiddler uses class weighting to address class imbalance. Compare two identical models–with and without weighting–to detect drift signals.
- Model Versions: Explore our guide to using Fiddler’s sample data to set up and manage multiple versions of a model with the powerful Model Versions feature.
- Ranking Models: Explore our notebook to see how Fiddler monitors ranking models using a public dataset organized around “search result impressions” from Expedia hotel searches.
- Regression: Check out our guide on using Fiddler to evaluate regression models. See examples of detecting issues using data drift and performance metrics like MAE.
- Feature Impact: Leverage this guide on using Fiddler’s feature impact upload API to supply your own feature impact values for your Fiddler model.
- CV Inputs: Explore our guide to using Fiddler’s monitoring for computer vision models. Learn to detect drift in image data with our unique Vector Monitoring approach.
ML Monitoring