- Store training, validation, and production datasets (features, predictions, actuals)
- Store performance metrics for each model version across environments
- Use any dataset as a baseline reference for monitoring production performance
Arize named a 2021 Gartner Cool Vendor for Enterprise AI
“The Cool Vendors in Enterprise AI, 2021” report, published by Gartner Analysts Chirag Dekate and Farhan Choudhary, highlights Arize’s ability to address the 3 key challenges inhibiting AI operationalization:
Automatically detecting problems such as data quality or drift issues.
Enabling faster root cause analysis and problem resolution of ML models.
Continuously improving model performance, interpretability and readiness.
According to the report, Arize should be on the radar of “enterprises seeking to maximize ROI and have visibility into how the model impacts your business bottom line, by increasing organizational focus on model building, improving model productionalization velocity and improving model outcomes.”
An ML observability solution for continuous model improvement
The ability to surface unknown issues and diagnose the root cause is what differentiates machine learning observability from traditional monitoring tools. By connecting datasets across your training, validation, and production environments in a central evaluation store, Arize enables ML teams to quickly detect where issues emerge and deeply troubleshoot the reasons behind them.
Explore the benefits of an evaluation store:
Environments
Evaluation Store
- Integrates with your feature store to track feature drift and data quality
- Integrates with your model store for a historical record of performance by model lineage
- Allows comparison of any production activity to any other model evaluation dataset (e.g. Test Set, Extreme Validation Set)
Monitoring & Data Checks
- Automatically detects drift, data quality issues, or anomalous performance degradations
- Highly configurable monitors based on both common KPIs and custom metrics
- Provides a centralized view of how a model acts on data for governance and repeatability
- Validates data distribution for extreme inputs/outputs, out of range, % empty, and other common quality issues
Performance Analysis
- Compares model performance across training, validation, and production environments
- Provides experimentation capabilities to test model versions
- Enables deep analysis and troubleshooting with slice & dice functionality
Explainability
- Uncovers underperforming cohorts of predictions
- Leverages SHAP values to expose feature importance
- Helps you understand when it’s time to retrain a model
