Comprehensive governance framework to control
and manage AI model Life cycle
Predict failure of Machine Learning models before it happens
When models fail, it can be a daunting time-consuming task to re-create the whole scenario and understand the root cause of failure. Lack of understanding business expectations, not following the best practices of Data Science and Machine Learning or simply due to a shift in customers’ behavior that may generate newer data points could be few of the reasons. Documenting them in an automated, audit-ready way can be an additional burden. Identifying and calculating drift in - Data, Features and Process to re-train the model requires a lot of manual effort and time.
MRM - Early warning indicator for model failure
With CyborgIntell’s MRM, you can now measure drift in quality of Data, Features, and Efficacy. Make accurate drift assessments and compare with baseline-data used for building the model. Getting to the root cause has never been easier, with all data-baselines being readily available, and drift measured in real time to proactively prompt stakeholders of a probable failure in model performance. With automated learning you can now approve for an incremental, partial, or complete retrain of the model.