Teams should compare model monitoring tools based on drift detection, data quality, model performance, alerting, explainability, integrations, scalability, governance, deployment, and cost.
- Evidently AI: Supports data quality checks, drift detection, model evaluation, and monitoring reports.
- Arize AI: Provides model performance monitoring, drift detection, segment analysis, and alerts.
- Fiddler AI: Offers model monitoring, explainability, drift analysis, and governance features.
- NannyML: Estimates model performance when actual outcome labels arrive late or remain unavailable.
- Alibi Detect: Provides drift, outlier, and adversarial detection for different data types.
- WhyLabs: Supports data quality monitoring, statistical profiling, drift detection, and alerts.
- MLflow: Supports model evaluation, experiment tracking, model management, and monitoring workflows.
- Amazon SageMaker Model Monitor: Provides monitoring for data quality, model quality, bias, and feature attribution.
- Prometheus and Grafana: Supports custom model metrics, dashboards, monitoring, and operational alerts.
- Deepchecks: Provides validation and monitoring checks for data and machine learning models.
Teams should also compare drift detection, performance tracking, data quality, alerting, explainability, integrations, security, scalability, deployment options, and total cost before selecting a platform.