Training, validating, and monitoring artificial intelligence and machine learning models

This invention describes a system for ensuring artificial intelligence and machine learning models are reliable before deployment. It works by first cleaning training data to remove biases, then using that data to train multiple models. These models are then rigorously tested with separate scoring data, including both quantitative and qualitative checks, and also against real-world production data, to pick the best performing one for use.

Why it matters: Filed before the widespread adoption of MLOps and the explosion of generative AI. The claims address critical needs for bias removal and robust validation, which are now central to deploying complex AI models responsibly and at scale.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedComplex AI/ML software system, data pipelines

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