Abstract: Systems and methods are disclosed for providing a CI/CD pipeline that decouples delivery of product configuration changes from code and similar changes, otherwise bundled/handled together with such configuration changes, for delivery to a running application in production. An exemplary method may include configuring components of CI/CD application in production to subscribe to application updates during runtime; processing an update to the application; processing/handling the update to process only configuration changes for propagation to subscribed components; validating the configuration changes; and instructing a workflow engine to: determine whether the configuration changes are needed for each component, update the components in runtime memory, if so, and provide the validated changes as an update in a next processing cycle to deploy such changes during the runtime.

Abstract: In some aspects, methods and systems may use consumption timestamps to ensure consistency between data used during inference and data used to train a machine learning model. Specifically, methods and systems may generate a consumption timestamp corresponding to the time at which the streaming data is received or inserted into a database. The consumption timestamp may be used as a synchronization point for any related data that may be requested (e.g., via an API). After streaming data is received, the consumption timestamp may be generated and assigned to any data that is requested in connection with the streaming data. The streaming data, related data, and consumption timestamp may be included in a data snapshot that is provided to a machine learning model for inference (e.g., real-time inference) or training.