What is Distributional?
Distributional provides a comprehensive framework for AI application testing. It enables teams to collect and augment data, conduct tests, alert on test results, triage failures, and resolve issues efficiently.
The platform features dashboards for analyzing results, capturing audit trails, and reporting outcomes for governance. Distributional's intelligence automates data augmentation, test selection, and calibration through an adaptive preference learning process. It can be deployed in your VPC and integrates seamlessly with your existing stack.
Features
- Data Collection and Augmentation: Enables the gathering and enhancement of data for thorough AI testing.
- Testing and Alerting: Facilitates comprehensive testing and provides alerts based on the results.
- Triage and Resolution: Streamlines the process of identifying, analyzing, and resolving issues.
- Dashboard Analysis: Offers dashboards for in-depth analysis of test results.
- Audit Trail and Governance: Captures audit trails and supports reporting for governance purposes.
- Automated Data Augmentation: Automates the data augmentation process using adaptive preference learning.
- Test Selection and Calibration: Automatically selects and calibrates tests.
Use Cases
- Testing the reliability of AI applications in enterprise environments.
- Mitigating risks associated with unpredictable AI systems.
- Ensuring compliance with governance requirements for AI deployment.
- Streamlining the AI testing process from data collection to issue resolution.
- Improving the overall quality and performance of AI and ML applications.
