Abstract: Systems and method for real-time keywords recommendations are provided. The systems and methods leverage one or more machine learning models that receive information about events that will occur in the future. The one or more machine learning models perform parallel processing to determine, in real-time and before the events occur, different keywords that are likely to experience an increase in usage based on the events. The one or more machine learning models also determine different types of content produced by content originators that are relevant to the determined keywords. Recommendations may be made for the content originator to have an association performed between the keywords and the content. Once the associations are performed, when a consumer inputs the keyword into an application, the consumer may be presented with the content or a mechanism (such as a hyperlink, for example) by which the consumer may access the content.

Abstract: Described are systems and methods for providing dynamic, adaptive, real-time traffic rules for applications employing a microservice architecture. Embodiments of the present disclosure can utilize historical and real-time traffic information, as well as real-time computing resource information, to determine flow and retry rules that are to be enforced for one or more services associated with an application. The flow and retry rules can be continuously and iteratively updated based on changing real-time traffic and computing conditions. Advantageously, embodiments of the present disclosure can mitigate failures encountered by client devices in response to application programming interfaces (API) calls to the various services.