What is MetricRule?
By integrating with existing model serving stacks such as Tensorflow Serving, KFServing, FastAPI, and Flask, MetricRule provides real-time insights into production features and predictions. Its metrics can be easily integrated into standard observability tools like Prometheus and Grafana, offering a comprehensive view of model behavior and health within the existing monitoring infrastructure.
Features
- Automatic Metric Creation: Generates metrics for ML service inputs and outputs automatically.
- Compatibility: Works with popular model serving stacks like Tensorflow Serving, KFServing, FastAPI, and Flask.
- Observability Integration: Pluggable into standard observability tools such as Prometheus and Grafana.
- Real-time Monitoring: Provides real-time data on production features and predictions.
- Open Source: Available as an open-source tool.
Use Cases
- Monitoring ML model performance in production.
- Detecting feature drifts in real-time.
- Identifying unexpected input data patterns.
- Getting alerted on poorly performing model deployments.
- Diagnosing model issues specific to data slices.
- Integrating ML model monitoring into existing observability stacks.
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