Machine Learning model governance at scale

Today, predictive models help many companies run their business-critical processes. These predictive models are probabilistic -- they involve chance variation -- and they depend on some underlying assumptions to make them work properly. Chief among these are 1.) that the data flowing into them is distributed in a certain way and 2.) that underlying business scenarios are unchanged from the time the models were created. As a result, it's important for companies to monitor changes to business scenarios and data quality, otherwise the model will no longer perform as intended. Additionally, it's important that these models comply with applicable laws and regulations once they are in production (active use).