Abstract: In one or more embodiments, a software service allows software providers to implement machine learning (ML) features into products offered by the software providers. Each ML feature may be referred to as an encapsulated ML application, which may be defined and maintained in a central repository, while also being provisioned for each user of the software provider on an as-needed basis. Advantageously, embodiments allow for a central definition for an ML application that encapsulates data science and processing capabilities and routines of the software provider. This central ML application delivers a ML deployment pipeline template that may be replicated multiple times as separate, tailored runtime pipeline instances on a per-user basis. Each runtime pipeline instance accounts for differences in the specific data of each user, resulting in user-specific ML models and predictions based on the same central ML application.

Abstract: Systems and methods described herein related to real-time management of service deployment systems are disclosed. A management system may monitor an infrastructure environment repository in real-time. Differences in configurations stored within the repository may be identified and used to generate configuration data. The configuration data may be used to update service operating parameters and service deployment configurations. The configuration data may be used to generate deployment data. The deployment data may be utilized to deploy one or more services on an updated infrastructure using the updated services.

Abstract: Disclosed herein is a system and method that determine whether activity pertaining to a software development project violates at least one development rule whose associated severity level exceeds a predetermined threshold.

Abstract: Disclosed herein is a system and method that determine whether activity pertaining to a software development project violates at least one development rule whose associated severity level exceeds a predetermined threshold.