# ML Monitoring

- [NLP Inputs](/developers/tutorials/ml-monitoring/simple-nlp-monitoring-quick-start.md): 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](/developers/tutorials/ml-monitoring/class-imbalance-monitoring-example.md): Discover how Fiddler uses class weighting to address class imbalance. Compare two identical models–with and without weighting–to detect drift signals.
- [Model Versions](/developers/tutorials/ml-monitoring/ml-monitoring-model-versions.md): 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](/developers/tutorials/ml-monitoring/ranking-model.md): Explore our notebook to see how Fiddler monitors ranking models using a public dataset organized around “search result impressions” from Expedia hotel searches.
- [Regression](/developers/tutorials/ml-monitoring/ml-monitoring-regression.md): 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](/developers/tutorials/ml-monitoring/user-defined-feature-impact.md): Leverage this guide on using Fiddler's feature impact upload API to supply your own feature impact values for your Fiddler model.
- [CV Inputs](/developers/tutorials/ml-monitoring/cv-monitoring.md): 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.