Accuracy

Track model performance to detect and react to data drift, improving model accuracy for better business outcomes.

Measure model performance with custom and out-of-the-box metrics for tabular, computer vision, and NLP models.

Detect, diagnose, and react to data drift before it impacts results with univariate & multivariate metrics.

Proactively improve model accuracy plus track and compare model versions over time.

"Thanks to Arthur, we know that our preventative care models are fair, and that we can catch any potential issues before they impact our members… and the Arthur platform allows us to detect and fix data drift before it becomes a real problem."

Heather Carroll Cox

Chief Analytics Officer, Humana

Monitor for Accuracy & Data Drift with Arthur

Autothresholding

Improve time to value for users onboarding highly complex models by automating thresholds for data drift detection.

Hotspots

Avoid future issues by automatically finding segments of the population where your model is underperforming, even if overall performance isn’t yet being affected.

Dynamic Segmentation

Give team members across MLOps and data science the ability to segment data in real time to understand model performance and data drift.

Learn more about improving ML model accuracy with Arthur

ML Model Monitoring
Data Drift Detection Part II: Unstructured Data in NLP and CV
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ML Model Monitoring
Data Drift Detection Part I: Multivariate Drift with Tabular Data
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ML Model Monitoring
Hotspots: Automating Underperformance Regions Surfacing in Machine Learning Systems
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