Abstract: Embodiments disclose systems and methods for generating source code. One or more application specific inputs and one or more external inputs are received. Using a machine learning model, source code is automatically generated based on the one or more application specific inputs and the one or more external inputs. The source code can embody the one or more application specific inputs and the one or more external inputs. One or more revisions to the automatically generated source code may be made, and a difference determined between the generated source code and the revised source code. The machine learning model may be further trained to generate a future source code based on the differences it determines.

Abstract: In some implementations, a system may receive, in a state event store, a deployment state event notification based on deployment of a new event consumer in the system. An old event consumer of the system may retrieve the deployment state event notification from the state event store. The system may deactivate an old event consumer based on retrieving the deployment state event notification, wherein the old event consumer refrains from processing topic event notifications after deactivation and continues to monitor the state event store for state event notifications. The old event consumer may publish a deactivation state event notification to the state event store based on deactivating the old event consumer. The new event consumer may retrieve the deactivation state event notification from the state event store. The system may activate the new event consumer based on retrieving the deactivation state event notification.

Abstract: A system determines that a primary event processor, included in a primary data center, is associated with a failure. The primary event processor is included in the primary data center and configured to process first events stored in a main event store of the primary data center. The system identifies a secondary event processor, in a secondary data center, that is to process one or more first events based on the failure. The primary event processor and the secondary event processor are configured to process a same type of event. The system causes, based on a configuration associated with the primary or secondary event processor, the one or more first events to be retrieved from one of the main event store or a replica event store. The replica event store is included in the secondary data center and mirrors the main event store of the primary data center.

Abstract: In some implementations, a system may receive a status associated with a service of a user account. The system may schedule, based on the status, a transmission of a message at an expiration of a time period, wherein the message is generated to indicate the status. The system may monitor, during the time period, the status to detect an event associated with the service. The system may detect, based on an occurrence of the event, an update to the status. The system may perform, prior to the expiration of the time period, an action associated with the message to account for the update to the status.

Abstract: Described herein is a method, system, and non-transitory computer readable medium for preventing data loss from both producer and consumer systems in continuous availability event-driven applications. The process for preventing data loss may replicate events at the broker level, and selectively receive replica events at the consumer level to account for the case the originally sent event is not received, while conserving computer and network resources. Alternatively, events and replica events may be received in duplicate. In either mode of reception, machine-learning may be used for implementing algorithms which further help to conserve resources and aid in preventing further data loss.

Abstract: In some implementations, a device may obtain historical information associated with user engagement with one or more historical communications associated with a user account. The device may train a machine learning model, using the historical information, to predict at least one of preferred communication channels, preferred communication timings, or preferred communication content associated with the user account. The device may determine that a communication associated with the user account is to be transmitted. The device may obtain, from the machine learning model and by the device, recommendation information including at least one of a recommended timing, a recommended communication channel, or a recommended content of the communication based on providing information associated with the user account to the machine learning model. The device may generate the communication according to the recommendation information.

Abstract: Systems, methods and media are directed to automatically generating a recommendation. Data describing a configuration of a production infrastructure is received, the production infrastructure running the system operating in the production environment. One or more metrics data values indicative of a performance of the system operating in the production environment is retrieved. Expected performance values of the system are received. An augmented decisioning engine compares the metrics data values with the expected performance values. The augmented decisioning engine is trained to provide a recommended configuration of the production infrastructure. Based on the comparing, the augmented decisioning engine is trained to improve subsequent recommendations of configuration of the production infrastructure through a feedback process.

Abstract: In some implementations, a device may obtain historical information associated with user engagement with one or more historical communications associated with a user account. The device may train a machine learning model, using the historical information, to predict at least one of preferred communication channels, preferred communication timings, or preferred communication content associated with the user account. The device may determine that a communication associated with the user account is to be transmitted. The device may obtain, from the machine learning model and by the device, recommendation information including at least one of a recommended timing, a recommended communication channel, or a recommended content of the communication based on providing information associated with the user account to the machine learning model. The device may generate the communication according to the recommendation information.

Abstract: An event processing system may identify duplicate events and prevent one or more of the duplicate events from being processed. The event processing system may determine the duplicate events, which are not permitted to be processed more than once, and allow these duplicate events to be processed exactly once. The event processing system may allow events, which are permitted to be processed more than once, to be processed without determining whether the events are duplicate events. If a main data center of the event processing system fails, a backup data center may take over instantly event processing from the main data center and may prevent events that have been processed by the main data center from being processed. Multiple data centers may work together and process events in an active-active mode by offering the exactly once processing as well as removing any duplicate events from upstream systems.

Abstract: Described herein is a method, system, and non-transitory computer readable medium for preventing data loss from both producer and consumer systems in continuous availability event-driven applications. The process for preventing data loss may replicate events at the broker level, and selectively receive replica events at the consumer level to account for the case the originally sent event is not received, while conserving computer and network resources. Alternatively, events and replica events may be received in duplicate. In either mode of reception, machine-learning may be used for implementing algorithms which further help to conserve resources and aid in preventing further data loss.

Abstract: Systems, methods and media are directed to automatically generating a recommendation. Data describing a configuration of a production infrastructure is received, the production infrastructure running the system operating in the production environment. One or more metrics data values indicative of a performance of the system operating in the production environment is retrieved. Expected performance values of the system are received. An augmented decisioning engine compares the metrics data values with the expected performance values. The augmented decisioning engine is trained to provide a recommended configuration of the production infrastructure. Based on the comparing, the augmented decisioning engine is trained to improve subsequent recommendations of configuration of the production infrastructure through a feedback process.

Abstract: In some implementations, a system may receive a status associated with a service of a user account. The system may schedule, based on the status, a transmission of a message at an expiration of a time period, wherein the message is generated to indicate the status. The system may monitor, during the time period, the status to detect an event associated with the service. The system may detect, based on an occurrence of the event, an update to the status. The system may perform, prior to the expiration of the time period, an action associated with the message to account for the update to the status.

Abstract: In some implementations, a device may obtain historical information associated with user engagement with one or more historical communications associated with a user account. The device may train a machine learning model, using the historical information, to predict at least one of preferred communication channels, preferred communication timings, or preferred communication content associated with the user account. The device may determine that a communication associated with the user account is to be transmitted. The device may obtain, from the machine learning model and by the device, recommendation information including at least one of a recommended timing, a recommended communication channel, or a recommended content of the communication based on providing information associated with the user account to the machine learning model. The device may generate the communication according to the recommendation information.

Abstract: In some implementations, a system may receive, in a state event store, a deployment state event notification based on deployment of a new event consumer in the system. An old event consumer of the system may retrieve the deployment state event notification from the state event store. The system may deactivate an old event consumer based on retrieving the deployment state event notification, wherein the old event consumer refrains from processing topic event notifications after deactivation and continues to monitor the state event store for state event notifications. The old event consumer may publish a deactivation state event notification to the state event store based on deactivating the old event consumer. The new event consumer may retrieve the deactivation state event notification from the state event store. The system may activate the new event consumer based on retrieving the deactivation state event notification.

Abstract: In some implementations, a system may receive a status associated with a service of a user account. The system may schedule, based on the status, a transmission of a message at an expiration of a time period, wherein the message is generated to indicate the status. The system may monitor, during the time period, the status to detect an event associated with the service. The system may detect, based on an occurrence of the event, an update to the status. The system may perform, prior to the expiration of the time period, an action associated with the message to account for the update to the status.

Abstract: Embodiments disclose systems and methods for generating source code. One or more application specific inputs and one or more external inputs are received. Using a machine learning model, source code is automatically generated based on the one or more application specific inputs and the one or more external inputs. The source code can embody the one or more application specific inputs and the one or more external inputs. One or more revisions to the automatically generated source code may be made, and a difference determined between the generated source code and the revised source code. The machine learning model may be further trained to generate a future source code based on the differences it determines.

Abstract: A system determines that a primary event processor, included in a primary data center, is associated with a failure. The primary event processor is included in the primary data center and configured to process first events stored in a main event store of the primary data center. The system identifies a secondary event processor, in a secondary data center, that is to process one or more first events based on the failure. The primary event processor and the secondary event processor are configured to process a same type of event. The system causes, based on a configuration associated with the primary or secondary event processor, the one or more first events to be retrieved from one of the main event store or a replica event store. The replica event store is included in the secondary data center and mirrors the main event store of the primary data center.

Abstract: In some implementations, a system may receive a status associated with a service of a user account. The system may schedule, based on the status, a transmission of a message at an expiration of a time period, wherein the message is generated to indicate the status. The system may monitor, during the time period, the status to detect an event associated with the service. The system may detect, based on an occurrence of the event, an update to the status. The system may perform, prior to the expiration of the time period, an action associated with the message to account for the update to the status.

Abstract: In some implementations, a system may receive, in a state event store, a deployment state event notification based on deployment of a new event consumer in the system. An old event consumer of the system may retrieve the deployment state event notification from the state event store. The system may deactivate an old event consumer based on retrieving the deployment state event notification, wherein the old event consumer refrains from processing topic event notifications after deactivation and continues to monitor the state event store for state event notifications. The old event consumer may publish a deactivation state event notification to the state event store based on deactivating the old event consumer. The new event consumer may retrieve the deactivation state event notification from the state event store. The system may activate the new event consumer based on retrieving the deactivation state event notification.

Abstract: In some implementations, a system may receive, in a state event store, a deployment state event notification based on deployment of a new event consumer in the system. An old event consumer of the system may retrieve the deployment state event notification from the state event store. The system may deactivate an old event consumer based on retrieving the deployment state event notification, wherein the old event consumer refrains from processing topic event notifications after deactivation and continues to monitor the state event store for state event notifications. The old event consumer may publish a deactivation state event notification to the state event store based on deactivating the old event consumer. The new event consumer may retrieve the deactivation state event notification from the state event store. The system may activate the new event consumer based on retrieving the deactivation state event notification.