Model observability for

Fraud detection

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

  • LLM monitoring

Just select the data quality metric that you need to monitor or build a custom integrity metric for your use case.

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.

LLM monitoring

LLM Monitoring`
Easily gain visibility into LLM and prompt/responce behaviors and detect drift, security issues, and privacy violations.

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