Arthur helps enterprise teams optimize model monitoring and performance at scale. Our platform tracks and improves model accuracy, explainability, and fairness across tabular, NLP, and CV models.
Arthur is model- and platform-agnostic, providing one centralized dashboard for all enterprise models, no matter which tools you used to build them or where they’re deployed.
Proactively detect poor model performance faster based on pre-set thresholds.
Give team members across MLOps and data science the ability to segment data in real time.
Avoid future issues by automatically finding segments of the population where your model is underperforming.
Save time by A/B testing model versions before moving to operations.
Improve time to value for users onboarding highly complex models by automating thresholds for data drift detection.
Consolidate dynamic model pipelines and project outcomes in one central place.
View all models and manage ML performance in a centralized dashboard.
Standardize MLOps performance metrics across teams.
Integrate all metrics into BI applications with our robust API service.
Quickly mitigate bias using Arthur's proprietary techniques, or customize with your own in-house metrics.
Set platform privacy and transparency rules based on enterprise security authentication.
Document and track enterprise ML in production with a historical timeline to identify model iterations and keep track of changes over time.
Actions can only be performed by authorized users. Individual teams/departments can have isolated environments with specific access control policies. Data is immutable once ingested, which prevents manipulation of metrics/insights.
Support for Single Sign-On with OIDC or SAML. Support for custom roles with granular permissions for users.
Arthur can be deployed to on-prem or private cloud environments, with no internet connectivity required. By default, Admins have no access to data, only to the platform components.
Data is end-to-end fully encrypted in transit into and out of the platform, as well as at rest in platform storage.