Abstract: A data processing system implements obtaining a set of input parameters associated with an update to be deployed to a plurality of server farms of a cloud-based service, wherein each server farm includes a primary replica configured to handle user traffic and a disaster recovery replica configured to handle user traffic responsive to a failure of the primary replica; determining temperature information for each of the server farms, ranking the server farms based on the temperature information to determine an order in which an update is to be deployed to the server farms; iteratively deploying the updates to the primary replicas of the server farms according to the ranking until an deployment threshold has been satisfied; and iteratively deploying the updates to the primary replicas of server farms for which the primary replicas have not yet been updated and to the disaster recovery replicas of the server farms.

Abstract: The techniques disclosed herein enable systems to safely deploy a plurality of upgrade variants to different resource units that provide a service by utilizing small-scale deployment and validation. To deploy upgrade variants, a system receives a selection of upgrade variants from a feature group and automatically selects an appropriate set of resource units at which to deploy the upgrade variants. The system is further configured to collect and analyze telemetry data from the set of resource units to determine if any problems have occurred as a result of the deployed upgrade variants. By analyzing the telemetry data, the system can also identify one or more upgrade variants that are causing the problems. In response, the system can remove the identified variants and proceed with deployment of the remaining upgrade variants.

Abstract: A data processing system implements obtaining a set of first input parameters associated with a first update to be deployed to one or more components of a cloud-based service; providing the set of first input parameters to a machine learning model to obtain a first deployment policy for the first update; analyzing the set of first input parameters using the machine learning model to generate the first deployment policy, the machine learning model being trained to analyze input parameters associated with an update to be deployed to the cloud-based service and to generate a deployment policy for the update, the deployment policy identifying a set of rings for deploying the update and when the update is to be deployed to a subset of the userbase of the cloud-based service associated with that ring; and executing the first deployment policy to deploy the update to the one or more components.

Abstract: Embodiments relate to providing at least one response to a user of a user device using a visual assistant, wherein the response is based on at least one user query. In an embodiment, the virtual assistant system receives the user query through a virtual multimedia contact, wherein the virtual multimedia contact is invoked by receiving a user command from the user through an interface. Thereafter, the virtual assistant system determines the response based on the user query. Further, the virtual assistant system selects a visual assistant (e.g., virtual character) for representing the response and subsequently provides the response to the user device using the visual assistant by transmitting the response to the user device using the visual assistant.

Abstract: A data processing system implements obtaining a set of input parameters associated with an update to be deployed to a plurality of server farms of a cloud-based service, wherein each server farm includes a primary replica configured to handle user traffic and a disaster recovery replica configured to handle user traffic responsive to a failure of the primary replica; determining temperature information for each of the server farms, ranking the server farms based on the temperature information to determine an order in which an update is to be deployed to the server farms; iteratively deploying the updates to the primary replicas of the server farms according to the ranking until an deployment threshold has been satisfied; and iteratively deploying the updates to the primary replicas of server farms for which the primary replicas have not yet been updated and to the disaster recovery replicas of the server farms.

Abstract: A data processing system implements obtaining a set of first input parameters associated with a first update to be deployed to one or more components of a cloud-based service; providing the set of first input parameters to a machine learning model to obtain a first deployment policy for the first update; analyzing the set of first input parameters using the machine learning model to generate the first deployment policy, the machine learning model being trained to analyze input parameters associated with an update to be deployed to the cloud-based service and to generate a deployment policy for the update, the deployment policy identifying a set of rings for deploying the update and when the update is to be deployed to a subset of the userbase of the cloud-based service associated with that ring; and executing the first deployment policy to deploy the update to the one or more components.

Abstract: The techniques disclosed herein enable systems to safely deploy a plurality of upgrade variants to different resource units that provide a service by utilizing small-scale deployment and validation. To deploy upgrade variants, a system receives a selection of upgrade variants from a feature group and automatically selects an appropriate set of resource units at which to deploy the upgrade variants. The system is further configured to collect and analyze telemetry data from the set of resource units to determine if any problems have occurred as a result of the deployed upgrade variants. By analyzing the telemetry data, the system can also identify one or more upgrade variants that are causing the problems. In response, the system can remove the identified variants and proceed with deployment of the remaining upgrade variants.

Abstract: Embodiments relate to providing at least one response to a user of a user device using a visual assistant, wherein the response is based on at least one user query. In an embodiment, the virtual assistant system receives the user query through a virtual multimedia contact, wherein the virtual multimedia contact is invoked by receiving a user command from the user through an interface. Thereafter, the virtual assistant system determines the response based on the user query. Further, the virtual assistant system selects a visual assistant (e.g., virtual character) for representing the response and subsequently provides the response to the user device using the visual assistant by transmitting the response to the user device using the visual assistant.

Abstract: Embodiments of the present disclosure relate to providing at least one response to a user of a user device using a visual assistant, wherein the at least one response is based on at least one user query. In an embodiment, the virtual assistant system receives the at least one user query through a virtual multimedia contact, wherein the virtual multimedia contact is invoked by receiving a user command from the user through an interface. Thereafter, the virtual assistant system determines the at least one response based on the at least one user query. Further, the virtual assistant system selects a visual assistant (virtual character) for representing the at least one response and subsequently provides the at least one response to the user device using the visual assistant by transmitting the at least one response to the user device using the visual assistant.

Abstract: Embodiments of the present disclosure relate to providing at least one response to a user of a user device using a visual assistant, wherein the at least one response is based on at least one user query. In an embodiment, the virtual assistant system receives the at least one user query through a virtual multimedia contact, wherein the virtual multimedia contact is invoked by receiving a user command from the user through an interface. Thereafter, the virtual assistant system determines the at least one response based on the at least one user query. Further, the virtual assistant system selects a visual assistant (virtual character) for representing the at least one response and subsequently provides the at least one response to the user device using the visual assistant by transmitting the at least one response to the user device using the visual assistant.