Abstract: Systems and methods for machine learning model retraining is described. In one example, a system includes a computing device that is configured to determine an anomaly score from a first input of a featurized data set for a machine learning model of a deployment environment. The computing device is configured to determine a feature correlation score for the machine learning model based at least in part on a second input of a featurized historical data set for the machine learning model. A model retraining frequency time period for the machine learning model is determined based at least in part on the feature correlation score and the anomaly score.

Abstract: A multi-tenant system stores data for customers. The multi-tenant system presents user interfaces allowing users associated with the customers to perform analysis of data stored for the customer. The multi-tenant system determines recommendations for subsequent user actions that can be performed by a user in a context. The context includes a report being analyzed, a type of visualization of the report, one or more interactions performed by the user with the report, and so on. The multi-tenant system presents one or more widgets based on the recommendations that allow the user to perform the recommended action. The multi-tenant system may determine a recommendation based on past interactions of a subset of users of the multi-tenant system, for example, users associated with a type of industry, users having a particular role in an organization, or a level of experience of the user with analysis of data.

Abstract: A multi-tenant system stores data for customers. The multi-tenant system presents user interfaces allowing users associated with the customers to perform analysis of data stored for the customer. The multi-tenant system determines recommendations for subsequent user actions that can be performed by a user in a context. The context includes a report being analyzed, a type of visualization of the report, one or more interactions performed by the user with the report, and so on. The multi-tenant system presents one or more widgets based on the recommendations that allow the user to perform the recommended action. The multi-tenant system may determine a recommendation based on past interactions of a subset of users of the multi-tenant system, for example, users associated with a type of industry, users having a particular role in an organization, or a level of experience of the user with analysis of data.