Abstract: A method comprises collecting parameters corresponding to processing by a first application programming interface of at least one application programming interface transaction, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, whether the at least one application programming interface transaction is anomalous. In the method, the first application programming interface is designated as being in an anomalous state responsive to predicting that the at least one application programming interface transaction is anomalous. One or more application programming interface requests for the first application programming interface are routed to a second application programming interface responsive to the anomalous state designation.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving information regarding a new sales opportunity from another computing device and determining one or more relevant features from the information regarding the new sales opportunity, the one or more relevant features influencing predictions of an opportunity outcome and an opportunity duration. The method also includes, by the computing device, generating, using a multi-target machine learning (ML) model, a first prediction of an opportunity outcome of the new sales opportunity and a second prediction of an opportunity duration of the new sales opportunity based on the determined one or more relevant features. The method may also include, by the computing device, sending the first and second predictions to the another computing device.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving a new application programming interface (API) specification and extracting one or more keywords from the new API specification. The method also includes identifying, using a trained machine learning (ML) model, one or more existing API specifications that are similar to the new API specification based on the one or more keywords from the new API specification and, responsive to the identification, outputting information regarding the one or more existing API specifications that are similar to the new API specification.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a computing device, receiving information regarding a new lead from another computing device and determining one or more relevant features from the information regarding the new lead, the one or more relevant features influencing prediction of a lead conversion. The method also includes, by the computing device, generating, using a machine learning (ML) model, a prediction of a likelihood of the new lead converting to a sales opportunity based on the determined one or more relevant features based on the determined one or more relevant features and sending the prediction to the another computing device.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving a new application programming interface (API) specification and extracting one or more keywords from the new API specification. The method also includes identifying, using a trained machine learning (ML) model, one or more existing API specifications that are similar to the new API specification based on the one or more keywords from the new API specification and, responsive to the identification, outputting information regarding the one or more existing API specifications that are similar to the new API specification.

Abstract: A method comprises collecting parameters corresponding to processing by a first application programming interface of at least one application programming interface transaction, analyzing the parameters using one or more machine learning algorithms, and predicting, based at least in part on the analyzing, whether the at least one application programming interface transaction is anomalous. In the method, the first application programming interface is designated as being in an anomalous state responsive to predicting that the at least one application programming interface transaction is anomalous. One or more application programming interface requests for the first application programming interface are routed to a second application programming interface responsive to the anomalous state designation.