Alibi Detect

Open source Python library focused on outlier, adversarial and drift detection.

Alibi Detect

Open source Python library focused on outlier, adversarial and drift detection.

Take control of model performance with advanced drift detection

Notice changes in data dynamics & define whether detected drift will cause a decrease in model performance.

Discover critical anomalies in input and output data using outlier detection

Alert business units and users when seeing unexpected behavior.

Adversarial detection ensures that models perform consistently.

Return the score that indicates the presence of features & instances that trick the model outcome.

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Features

Workflows

Workflows

Front-end deployment of models, explainers and canaries means non-Kubernetes experts can deploy ML models and testing can be done in live environments.

Model management

Model management

Metrics and dashboards can monitor models to improve performance and rapidly communicate errors for easy debugging.

Model confidence

Model confidence

Model explainers mean you can understand and adjust what features are influencing the model and anomaly detection can flag drifts in data and alert users to adversarial attacks.

Stack stability

Stack stability

Backwards compatibility, rolling updates and full SLA alongside maintained integrations with all frameworks and clouds means a seamless install and reliable infrastructure.

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Get Started

You’ll find Alibi Detect on GitHub 

GitHub

See it for yourself

Deploy, monitor and explain your models today.

Solutions

Open Source

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