Veritable AI Quality Platform
Our AI Quality Management solutions start with a powerful core.
Strong fundamentals drive great model quality results
Veritable AI Quality Platform Overview
The right platform leads to the best results
Enterprise class explainability
Based on six years of research performed at Carnegie Mellon University, the Veritable AI Quality Platform performs sophisticated sensitivity analysis that enables data scientists, business users, and risk and compliance teams to understand exactly why a model makes predictions.
Model quality analytics
Model quality helps ensure models achieve the intended business impact. Veritable analyzes several facets of model quality including:
- Conceptual soundness
Review and governance workflow
High-stakes and regulated models often require separate model validation or governance processes. Veritable includes best practices for validation and governance, including model documentation, auditability, and reproducibility.
Model comparisons and selection
Machine learning development is highly iterative and experimental, so quickly understanding the evolution of model versions is critical. With Veritable, data scientists can more deeply and easily compare models than ever before, enabling them to extract insights to guide faster and more effective model development.
Reporting and monitoring
Over time, model performance can change due to changes in the underlying data or the implications of that data. The Veritable AI Quality Platform makes it easy for data scientists to monitor and understand data drift, concept drift, and model quality over time.
Easy to deploy
Veritable embeds easily within your existing infrastructure and workflow
- Deploy on premises or in your cloud, including private cloud, AWS, Google, or Azure
- Integrate easily with popular model development and model serving solutions
- Scale to meet high model volumes
- Export data via APIs to BI tools such as Tableau and Looker