Abstract: A post-deployment drift detection monitoring of a predictive model is described. The method includes accessing a predictive performance metric of a machine learning model that is deployed at a server, the machine learning model being trained with an initial set of training data containing historical data, the predictive performance metric being based on the initial set of training data and an additional set of training data, the additional set of training data containing training data collected since training the machine learning model, detecting a drift based on the predictive performance metric exceeding a drift detection threshold, generating a drift warning notification to a client device, the drift warning notification indicating that the predictive performance metric exceeds the drift detection threshold, receiving a user feedback from the client device, and adjusting one of the machine learning model or the drift detection threshold based on the user feedback.

Abstract: A system for private and secure data portal is described. A method includes receiving, from a first client device, a permission request and a data request for a first dataset that is stored at a second client device, providing the permission request and the data request to the second client device, the second client device configured to generate, in response to the permission request and the data request, a data usage approval document and an encrypted synthesized dataset corresponding to the data usage approval document, the encrypted synthesized dataset includes a synthetic second dataset representative of the first dataset, receiving, from the second client device, the data usage approval document and the encrypted synthesized dataset, performing, at a server, a computation on the encrypted synthesized dataset based on the data request, and providing the data usage approval document and results of the computation to the first client device.

Abstract: A machine learning platform operating at a server is described. The machine learning platform accesses a dataset from a datastore. A task that identifies a target of a machine learning algorithm from the machine learning platform is defined. The machine learning algorithm forms a machine learning model based on the dataset and the task. The machine learning platform deploys the machine learning model and monitors a performance of the machine learning model after deployment. The machine learning platform updates the machine learning model based on the monitoring.