Abstract: A system automatically generates resiliency tests to detect the resiliency of an application implemented over multiple machines. In operation, one or more services operating on a plurality of machines is automatically identified. Fault targets are then automatically created based on the identified one or more services. The generation of the fault targets may be based on the identified services, user input, or past performance of the plurality of machines. Experiments may then be conducted, based on the created targets, on the one or more services. The experiments may insert faults into resources associated with the services. The resources may include CPU resources, memory, networking resources, and APIs. A resiliency score may be generated based on the experiments.

Abstract: A system automatically generates resiliency tests to detect the resiliency of an application implemented over multiple machines. In operation, one or more services operating on a plurality of machines is automatically identified. Fault targets are then automatically created based on the identified one or more services. The generation of the fault targets may be based on the identified services, user input, or past performance of the plurality of machines. Experiments may then be conducted, based on the created targets, on the one or more services. The experiments may insert faults into resources associated with the services. The resources may include CPU resources, memory, networking resources, and APIs. A resiliency score may be generated based on the experiments.

Abstract: The disclosed technology relates identifying causes of an observed outcome. A system is configured to receive an indication of a user experience problem, wherein the user experience problem is associated with observed operations data including an observed outcome. The system generates, based on the observed operations data, a predicted outcome according to a model, determines that the observed outcome is within range of the predicted outcome, and identifies a set of candidate causes of the user experience problem when the observed outcome is within range of the predicted outcome.

Abstract: The disclosed technology relates identifying causes of an observed outcome. A system is configured to receive an indication of a user experience problem, wherein the user experience problem is associated with observed operations data including an observed outcome. The system generates, based on the observed operations data, a predicted outcome according to a model, determines that the observed outcome is within range of the predicted outcome, and identifies a set of candidate causes of the user experience problem when the observed outcome is within range of the predicted outcome.

Abstract: The disclosed technology relates identifying causes of an observed outcome. A system is configured to receive an indication of a user experience problem, wherein the user experience problem is associated with observed operations data including an observed outcome. The system generates, based on the observed operations data, a predicted outcome according to a model, determines that the observed outcome is within range of the predicted outcome, and identifies a set of candidate causes of the user experience problem when the observed outcome is within range of the predicted outcome.

Abstract: The disclosed technology relates identifying causes of an observed outcome. A system is configured to receive an indication of a user experience problem, wherein the user experience problem is associated with observed operations data including an observed outcome. The system generates, based on the observed operations data, a predicted outcome according to a model, determines that the observed outcome is within range of the predicted outcome, and identifies a set of candidate causes of the user experience problem when the observed outcome is within range of the predicted outcome.

Abstract: The disclosed technology relates identifying causes of an observed outcome. A system is configured to receive an indication of a user experience problem, wherein the user experience problem is associated with observed operations data including an observed outcome. The system generates, based on the observed operations data, a predicted outcome according to a model, determines that the observed outcome is within range of the predicted outcome, and identifies a set of candidate causes of the user experience problem when the observed outcome is within range of the predicted outcome.

Abstract: The disclosed technology relates identifying causes of an observed outcome. A system is configured to receive an indication of a user experience problem, wherein the user experience problem is associated with observed operations data including an observed outcome. The system generates, based on the observed operations data, a predicted outcome according to a model, determines that the observed outcome is within range of the predicted outcome, and identifies a set of candidate causes of the user experience problem when the observed outcome is within range of the predicted outcome.

Abstract: In one embodiment, a method of processing a natural language input using a conversational knowledge graph in a virtual assistant is disclosed. The method includes receiving a natural language query from a user; translating the natural language query received from the user into corresponding intents; retrieving conversational knowledge context information based on the intents; using the retrieved conversational knowledge context information to customize back-end service calls to downstream applications; receiving a result of the customized back-end service calls; sending the result of the customized back-end service calls in a response to the natural language understanding system; translating the response from the fulfillment service system into a natural language response; and providing the natural language translated response to the user.

Abstract: In one embodiment, a method of processing a natural language input using a conversational knowledge graph in a virtual assistant is disclosed. The method includes receiving a natural language query from a user; translating the natural language query received from the user into corresponding intents; retrieving conversational knowledge context information based on the intents; using the retrieved conversational knowledge context information to customize back-end service calls to downstream applications; receiving a result of the customized back-end service calls; sending the result of the customized back-end service calls in a response to the natural language understanding system; translating the response from the fulfillment service system into a natural language response; and providing the natural language translated response to the user.

Abstract: The disclosed technology relates identifying causes of an observed outcome. A system is configured to receive an indication of a user experience problem, wherein the user experience problem is associated with observed operations data including an observed outcome. The system generates, based on the observed operations data, a predicted outcome according to a model, determines that the observed outcome is within range of the predicted outcome, and identifies a set of candidate causes of the user experience problem when the observed outcome is within range of the predicted outcome.

Abstract: The disclosed technology relates to machine learning and statistical models. A system is configured to receive a user statement comprising a request for information and identify an intent type and one or more parameters based on the user statement. The system selects a model from the model registry based on the intent type and the one or more parameters, obtains a result based on invoking the selected model using the one or more parameters, and provides the result to the user.