Monitaur combines the first and only software built specifically to drive assurance and governance of machine learning with our deep ML audit and systems expertise.
Monitaur Record creates a single source of truth for ML transactions across your applications, the foundation for understanding what your ML is actually doing. Accelerate forensic discovery for audit and compliance, reveal ML decisioning, and get transparency across all of your ML deployments.
Capture the unique parameters behind every ML decision
See which models and code files produced every ML decision
Generate explainability models for ML decisions automatically
Monitaur Audit ensures objective verifiability of your ML models and systems by enabling non-technical users to find, review, and test ML decisions. Risk, compliance, and governance owners can self-serve, auditors and regulators can inspect with exposing IP, all without demanding effort from your technical teams.
Enable sampling and spot testing of ML transactions
Examine relationships between outcomes and inputs
Enable "what-if" scenario analysis of ML decisions
No matter how much work you invest in pre-production, you need ongoing visibility into their decisions and the movement of the models. With our Monitor capabilities, you can identify potential risks proactively, empower compliance to operate continuously, and maximize the performance and security of your ML systems.
Implement controls through custom thresholds
See when ML introduces bias and outliers
Use statistical tests for bad inputs and outputs
Bolster existing processes for model and code changes
The last mile of assurance will always rely on human intelligence to provide the level of comfort that business owners need to greenlight ML initiatives.
Monitaur Assure brings our expertise in ML assurance to your organization. We provide services to complement our software technology to ensure your team and your program can create optimal assurance.
Use our MLA framework to establish controls, governance, policies, and monitoring
Identify gaps and provide recommendations for proper design, documentation, and operation of your ML models