Standard 4 stops to get here
Model Monitoring
Tracking model performance, data distribution, and predictions in production to detect issues and degradation.
Your route here
4 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Inference ✓ understood
Running a trained model on new inputs to get predictions, with its weights frozen: the stage of a model's life that users actually interact with.
- Model Serving ✓ understood
Deploying trained models as services that can handle prediction requests in production environments.
- Model Monitoring · you are here ✓ understood
Where it sits
Explore nearby
Shipping AI Data Drift Changes in input data distribution over time that can degrade model performance in production. Shipping AI Model Drift Degradation of model performance over time due to changes in the relationship between features and target. Shipping AI MLOps Practices for deploying, monitoring, and maintaining machine learning models in production, combining ML and DevOps principles. Shipping AI Model Performance Degradation Decline in model quality over time due to distribution shift or changing patterns. Shipping AI Model Retraining Periodically updating models with new data to maintain performance as distributions change.