Reduced Risk of Model Misuse
Prevent unauthorized access and deployment, mitigating the risk of biased, inaccurate, or malicious models being used in production.
Improved Model Fairness and Transparency
Governance policies promote fairness by encouraging bias detection and mitigation throughout the model lifecycle. Access control ensures only authorized personnel can modify models.
Cost-Effectiveness
Efficient access control minimizes the risk of errors or security breaches, potentially reducing operational costs.
Granular User Permissions
Define various granular permissions for each user. This allows for a flexible and secure approach based on user responsibilities. Control who can register new models or create versions of the existing models in the system. Permissions might range from "view only" to "create and edit" models, ensuring only authorized users can introduce new models. With customizable access settings, organizations can confidently manage their resources and achieve a productive research environment.
Approval Workflows
Integrate approval workflows into the deployment process. Specific roles might need to approve deployments before they go live, adding an extra layer of control. By integrating approval workflows into the deployment process, organizations can enforce governance, mitigate risks, and maintain control over the models deployed within their infrastructure, ultimately promoting trust, transparency and confidence in the AI ecosystem.
Deployment Environment Permissions
Define who can deploy models to different environments (e.g., development, testing, production). This restricts unauthorized deployments, ensures models go through proper testing stages and allows an organization to have a controlled model deployment ecosystem. These permissions ensure secure and efficient deployment processes, allowing teams to streamline workflows, mitigate risks, and maintain compliance standards.
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