Explore Machine Learning Assurance in depth
Machine Learning presents a fundamentally different challenge for governance, risk, compliance, and audit professionals. How do you effectively do your vital work when you can't understand, inspect, and test the decisions made by ML systems?
You can with Machine Learning Assurance (MLA), a controls-based process for ML systems that establishes confidence and verifiability through software and human oversight.
Read our white paper for a deeper introduction to Machine Learning Assurance and an overview of how Monitaur's software and service offerings can support your assurance efforts.
- Why you should pursue assurance focused specifically on machine learning
- How regulatory developments are driving the need for Machine Learning Assurance
- How the core principles create assurance
- How to use CRISP-DM as a framework for MLA
- How MLA can be deployed in industry-specific use cases
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