Please see the video of how Model Monitoring is possible in BDB Platform
Model observability refers to the ability to understand and monitor the behavior and performance of a machine learning model during its deployment. It involves tracking and analyzing various aspects of the model's functioning to ensure that it meets the desired criteria and continues to perform well over time.
Key aspects of model observability include:
- Monitoring Inputs and Outputs
- Performance Metrics
- Data Drift Detection
- Model Health Checks
- Error Analysis
- Logging and Auditing
Effective model observability is crucial for maintaining the reliability and trustworthiness of machine learning systems, especially in real-world, dynamic environments where data distributions and model requirements can change over time.
