Monitaur leads the way in end-to-end AI and model governance. Our goal is to enable independently verifiable proof that your critical modeling systems are functioning responsibly and as expected.
Our cloud-based applications are SOC2 Type II certified to provide complete, user-friendly processes for your teams and entire modeling project lifecycle.
From GLM to LLM, modeling systems present a new paradigm of technology and business decisioning.
Individuals and organizations responsible for managing risk and compliance – including regulators – have not previously contended with such dynamic and variable models and systems.
Artificial intelligence and machine learning models are being used to make important decisions that affect people’s lives, jobs, and chances. This means we need to trust and believe in these systems. There should be a reasonable ability to evaluate and verify their safety, fairness, and compliance.
Companies, regulators, and consumers all benefit from an objective method of assuring modeling systems.
Models can behave unexpectedly, creating enormous liabilities and risk for unforeseen damage and reputational harm.
Modeling systems are opaque to non-technical professionals, requiring more ongoing attention from objective parties to ensure safety and compliance.
Lack of governance over models and their outcomes can lead to broad misconceptions and bias, undermining the long-term promise of the technology.
Core pillars support an effective governance and responsible use of models.
Governance requires clear understanding and documentation of the considerations, goals, and risks evaluated throughout the modeling lifecycle.
MLA requires every business and technical decision has the ability to be verified and interrogated.
MLA requires any ML application can be reasonably evaluated and understood by an objective individual or party not involved in the model development.
A governed modeling system requires a continuous, coherent approach throughout the lifecycle of projects, as well as across your enterprise operations. Careful coordination of people, processes, and systems can create the clarity, confidence, and accountability that practitioners need.
Organizations can utilize the established, effective CRISP-DM steps and deploy detective controls vital for AI and machine learning systems to drive a powerful framework for assurance and robust risk and control matrices.
Evaluate key business drivers and questions at project inception and revisit throughout.
Business Goals
Business Context
Understand data lineage, quality, and usage rights.
Data Governance
Pre-process standardized data sets for training, test, and production.
Data Preparation
Data Segmentation
Create the simplest, best fit, and performant models.
Algorithm Selection
Cross-Functional Review
Metric Selection
Determine accuracy and precision of models before launch and continuously in production.
Model Validation
Deliver business sign-off and develop capacity for continuous inspection and review.
Executive Accountability
Monitoring Process
Model Logging & Interpretability