As AI adoption grows, regulatory pressure also increases. Make sure your Machine Learning models are under control and compliant with forthcoming regulations.
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Productionalize ML models, ensuring fairness and compliance
High-risk AI systems that directly affect citizens’ lives, such as credit scoring or assisted medical decisions, are under regulatory pressure. In order to comply with regulations, AI systems should be fair, transparent, and secure.
Regulation is complex and hard to keep track of
Access compliance insights
- Compliance guidance before and after deployment with automated checklists
- Efficient and automated sign-off flow for stakeholders to accelerate model deployment
- Regular new checklists to keep up with new regulation
Regulation calls for full model documentation
Ensure model accountability
- Store model documentation in a central environment
- Log every change in deployed models
- Traceback predictions/decisions
- Keep track of model information with model cards
Regulations mandate transparency and human oversight
Oversee & explain model decisions
- Ensure model predictions are explainable to stakeholders
- Allow stakeholders to give feedback, approve or overrule predictions
- Utilize the collected feedback to improve and retrain models
Regulation stresses the need for model accuracy
Manage and monitor performance
- Get alerts on monitoring model drift, bias detection, and performance
- Define custom alerts for the metrics that matter to you
- Easily discover the root cause of model degradation
Find a plan that suits your needs
