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Streamline your model observability
Get in minutes what used to take years to build.
Simple, customizable, scalable, secure, ML monitoring.
Data quality
Input drift
Bias monitoring
Performance analysis
Model shifts
Explainability
Version comparison
Anomaly investigation
Observability management
Just select the data quality metric that you need to monitor or build a custom integrity metric for your use case.
Customize your drift metrics from a-z – distance functions, features, datasets, timeframes, sensitivity, and much more.
Build any bias metric your business needs and monitor them across different protected classes and sub-groups.
Track performance continuously and analyze changes, and drill into behavior segment by segment.
Easily identify and investigate model shifts and drill down into granular data to pinpoint the root cause.
Explain model behaviors on the global, cohort, and individual decision level.
Analyze and compare versions, datasets, and production timeframes to detect changes.
Correlate and group anomalies to quickly pinpoint casualty and resolve issues before they impact your business.
Centralized model monitoring management per use case. Build segments, manage configurations, and create monitors once for multiple models.
Data quality
Just select the data quality metric that you need to monitor or build a custom integrity metric for your use case.
Input drift
Customize your drift metrics from a-z – distance functions, features, datasets, timeframes, sensitivity, and much more.
Bias monitoring
Build any bias metric your business needs and monitor them across different protected classes and sub-groups.
Performance analysis
Track performance continuously and analyze changes, and drill into behavior segment by segment.
Model shifts
Easily identify and investigate model shifts and drill down into granular data to pinpoint the root cause.
Explainability
Explain model behaviors on the global, cohort, and individual decision level.
Version comparison
Analyze and compare versions, datasets, and production timeframes to detect changes.
Anomaly investigation
Correlate and group anomalies to quickly pinpoint casualty and resolve issues before they impact your business.
Observability management
Centralized model monitoring management per use case. Build segments, manage configurations, and create monitors once for multiple models.
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3Start monitoring
Fraud detection
Entire population drift – high probability of concept drift. Open incident investigation →
Fraud detection
Segment “tablet shoppers” drifting. Split model and retrain.
Don’t just take our word for it
“We use Superwise on a daily basis to get transparency and detect drift and model decay. Enabling us to better understand changes, connect them to real events and data bugs, and make better decisions and move even faster than before.
“Superwise named a Gartner Cool Vendor in the 2020 Enterprise AI Governance report.
“Today, we use Superwise to monitor over 6K metrics in real-time. Giving us control over our dynamic business and peace of mind that we’ll always know about unwanted issues.
“Superwise was highlighted as an MLOps solution vendor in Forrester’s 2020 “Introducing ModelOps to Operationalize AI” report.
“By selecting Superwise we can ensure accuracy and efficiency of our data science efforts.
“Superwise is part of the NVIDIA inception program.
“We’re excited to partner with Superwise. Superwise’s model observability integration with Datadog will help MLOps teams ensure models maintain calibration and accuracy during their lifetime in production.
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