Abstract: A feature management system (the “system”) receives information about a new machine learning (ML) model to be trained. The information includes metadata about the new model. The system applies a trained feature prediction model to the information about the new model and metadata about a plurality of features. The feature prediction model is trained to predict a probability that each of the plurality of features should be selected as an input feature for the new model. The feature management system identifies one or more candidate features based on an output probability score of the feature prediction model. The system presents in a user interface a suggestion to use the one or more candidate features with the new model. The system selects at least one candidate feature and causes the new model to be trained using a set of input features, including the selected candidate feature.

Abstract: A system maintains a data store for managing machine-learning (ML) models and features that are used by the models. The system generates a graph including nodes for each model and a node for each feature, and edges linking models and features that are used by the models. For a new model to be trained, the system receives a proposed feature corresponding to a node in the graph, and identifies one or more candidate features corresponding to nodes in the graph based in part on relevancy scores between the proposed feature with other features corresponding to nodes in the graph. The system presents in a user interface a suggestion to use one or more candidate features with the new model. Responsive to receiving a user selection of at least one candidate feature, the system causes the new model to be trained using the selected candidate feature and the proposed feature.

Abstract: An online system facilitates various functions using machine learning model microservices. A tuning mechanism tunes various configuration parameters for each microservice that control allocation of computing resources and other configurations of physical and/or virtual machines that implement the microservices. Tuning may be performed in part by executing tests under various configurations and evaluating an objective function associated with the different configurations. Furthermore, parameters of the objective function may be set based on a trained learning model that learns baseline parameters and weights of the objective function based on historical data.

Abstract: Provided herein are biomarkers that can be altered in the gut of a mammal having CDI. One or more biomarkers can be used to predict Clostridium difficile infection (CDI) treatment outcome (e.g., response to primary CDI treatment and/or risk of recurrence after primary CDI treatment) in a mammal (e.g., a human) having CDI. Methods for using one or more biomarkers can be used to predict response to primary CDI treatment in a mammal having CDI. Methods for using one or more biomarkers can be used to predict risk of recurrence in a mammal having CDI. Also provided are methods of treating CDI.

Abstract: Provided herein are biomarkers that can be altered in the gut of a mammal having CDI. One or more biomarkers can be used to predict Clostridium difficile infection (CDI) treatment outcome (e.g., response to primary CDI treatment and/or risk of recurrence after primary CDI treatment) in a mammal (e.g., a human) having CDI. Methods for using one or more biomarkers can be used to predict response to primary CDI treatment in a mammal having CDI. Methods for using one or more biomarkers can be used to predict risk of recurrence in a mammal having CDI. Also provided are methods of treating CDI.