MLOps shouldn't stop at deployment

Get the most out of your deployed models with added transparency and control over model decisions, fostering trust and understanding through clear explanations for predictions.

Ensure stability of model performance

When your data changes so can your models predictions. Ensure accuracy by monitoring alteration.

Strengthen intuition for feature selection

Gain insights into how features influence model performance.

Derive a set of features and attributes for consistent prediction

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

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Start using Alibi Explain today through GitHub. You’ll only need a license for production use. It’s free for all non-production and academic uses. 

Highlights

Feature Alteration

Feature Alteration

See how prediction changes and ensure stability of model performance against changing data.

Feature Impact

Feature Impact

Alibi indicates how features influence model performance, strengthening intuition for feature selection.

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Necessary Features

Focus on critical data attributes and features by deriving a set of features and attributes for consistent prediction.

<span style="font-family: "FS Aldrin Light", sans-serif; font-size: 35px; letter-spacing: normal; text-align: center; white-space-collapse: collapse;">Feature Attribution </span>

Feature Attribution 

Build confidence in integrity of model performance when Alibi illustrates prediction dynamics when features change.

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