Abstract: A method, a device, and a non-transitory storage medium are described in which a machine learning-based project evaluation, prediction, and recommendation service is provided. The service may include obtaining performance data pertaining to key performance indicators (KPIs) and groups of a project and calculating affinity propagation using the performance data. The service may further include calculating prospective target KPI values for groups that may have deficient current KPI values based on similar performance data associated with other groups. The service may also include calculating time periods allotted to attain the prospective target KPI values.

Abstract: One or more computing devices, systems, and/or methods for adaptive API call sequence detection are provided. A series of API calls and gap times between API calls of the series of API calls are recorded. The API calls are received and processed by a production system. The API calls are assigned into API call sequences. An end of an API call sequence is detected based upon a minimum response time and the gap times between the API calls. The API call sequences are utilized to simulate execution of the production system. A configuration is generated and applied to the production system based upon a result of the simulation.