Abstract: In some examples, a system can use machine learning to schedule one or more tasks to be performed within a resource constraint. The system can train a machine-learning model based on training data that can include historical execution data for historical tasks performed by one or more entities. The system can receive a request including the tasks and can generate input data based on the request. The system can provide the input data to the trained machine-learning model, thereby obtaining an output indicating a respective execution outcome of each entity with respect to performing at least one task. Based on the output, the system can determine that the entities are unable to individually perform at least one task within the resource constraint. The system can perform an action to resolve the inability of the entities to perform the at least one task within the resource constraint.

Abstract: In some examples, a system can use machine learning to schedule a task of one or more tasks to be performed by a target entity within a resource constraint. The system can access training data that can include historical execution data associated with one or more historical tasks performed by one or more entities. The system can train a machine-learning model based on the training data to generate a trained machine-learning model. The system can receive a request including the tasks that are each schedulable to be performed by the entities and can generate input data based on the request. The system can provide the input data to the trained machine-learning model such that the trained machine-learning model can generate an output indicating the target entity selected to perform the task. The system can schedule the task to be performed by the target entity with the resource constraint.

Abstract: An artificial intelligence chat bot can be used to configure a cloud computing system. For example, a system can receive, from a user, a natural language input expressing a target functionality for a distributed computing system. A first machine learning model, trained on a natural language dataset to identify target functionalities, can be used to generate an output indicating the target functionality based on the input. A second machine learning model, trained on configuration data and support documentation to identify configurations and adjustments, can be used to generate an output including a recommendation to adjust an existing configuration of the distributed computing system based on a difference between the target functionality and the existing configuration. The adjustment can include a modification enabling the distributed computing system to perform the target functionality. The system can generate response including the recommendation to be output to the user.

Abstract: An artificial intelligence chat bot can be used to configure a cloud computing system. For example, a server system can determine, using a first machine learning model, a first selection of a first parameter for configuring a distributed computing system based on a first input provided by a user in a natural language format. The server system can determine, using a second machine learning model, an additional set of parameters for the distributed computing system. The server system can output the additional set of parameters for display to the user in the natural language format. The server system can determine, using the first machine learning model, a second selection of a second parameter of the additional set of parameters based on a second input provided by the user. The server system can automatically deploy the distributed computing system configured with the first parameter and the second parameter.

Abstract: An artificial intelligence chat bot can be used to configure a cloud computing system. For example, a system can determine, using a first machine learning model, a target functionality for a distributed computing system based on an input provided by a user in a natural language format expressing the target functionality. The system can determine, using a second machine learning model, a recommended adjustment to an existing confirmation of the distributed computing system based on an incompatibility between the target functionality and a configuration setting of a software service executed by the distributed computing system. The system can output a response to the natural language input comprising the recommendation to the user in the natural language format. The system can automatically deploy or reconfigure the distributed computing system on the recommendation.

Abstract: An artificial intelligence chat bot can be used to configure a cloud computing system. For example, a server system can determine, using a first machine learning model, a first selection of a first parameter for configuring a distributed computing system based on a first input provided by a user in a natural language format. The server system can determine, using a second machine learning model, an additional set of parameters for the distributed computing system. The server system can output the additional set of parameters for display to the user in the natural language format. The server system can determine, using the first machine learning model, a second selection of a second parameter of the additional set of parameters based on a second input provided by the user. The server system can automatically deploy the distributed computing system configured with the first parameter and the second parameter.

Abstract: A search query can be resolved efficiently using stored key information in a data cache. A system can receive a search query via an application programming interface (API). The search query can comprise a set of query parameters. The system can select at least one interaction event search service. The system can identify a plurality of interaction events relevant to the search query. Each interaction event can include a description and key information used to locate each interaction event. The system can retrieve the key information for each interaction event. The system can store, in a data cache, the key information for each interaction event. Subsequent to retrieving the key information, the system can retrieve, using at least a portion of the key information, descriptions of a first portion of interaction events. Additionally, the system can transmit the descriptions of the first portion of interaction events to the user.

Abstract: Some examples can automatically detect functional anomalies associated with software services of a distributed application. For example, a system can receive a configuration file indicating an execution sequence for a set of services of a distributed application. The configuration file can include a mapping of a set of endpoint addresses to the set of services. The system can also receive a mixed set of debugging data associated with the set of services. The system can then parse the mixed set of debugging data into groups corresponding to the set of services based on the mapping in the configuration file. The system can then determine a sequence of debugging events by analyzing the groups, detect a functional anomaly associated with the set of services based on the sequence of debugging events, and generate an alert indicating the functional anomaly to a user.

Abstract: Some examples can automatically detect functional anomalies associated with software services of a distributed application. For example, a system can receive a configuration file indicating an execution sequence for a set of services of a distributed application. The configuration file can include a mapping of a set of endpoint addresses to the set of services. The system can also receive a mixed set of debugging data associated with the set of services. The system can then parse the mixed set of debugging data into groups corresponding to the set of services based on the mapping in the configuration file. The system can then determine a sequence of debugging events by analyzing the groups, detect a functional anomaly associated with the set of services based on the sequence of debugging events, and generate an alert indicating the functional anomaly to a user.

Abstract: A search query can be resolved efficiently using stored key information in a data cache. A system can receive a search query via an application programming interface (API). The search query can comprise a set of query parameters. The system can select at least one interaction event search service. The system can identify a plurality of interaction events relevant to the search query. Each interaction event can include a description and key information used to locate each interaction event. The system can retrieve the key information for each interaction event. The system can store, in a data cache, the key information for each interaction event. Subsequent to retrieving the key information, the system can retrieve, using at least a portion of the key information, descriptions of a first portion of interaction events. Additionally, the system can transmit the descriptions of the first portion of interaction events to the user.

Abstract: An artificial intelligence chat bot can be used to configure a cloud computing system. For example, a server system can determine, using a first machine learning model, a first selection of a first parameter for configuring a distributed computing system based on a first input provided by a user in a natural language format. The server system can determine, using a second machine learning model, an additional set of parameters for the distributed computing system. The server system can output the additional set of parameters for display to the user in the natural language format. The server system can determine, using the first machine learning model, a second selection of a second parameter of the additional set of parameters based on a second input provided by the user. The server system can automatically deploy the distributed computing system configured with the first parameter and the second parameter.

Abstract: An artificial intelligence chat bot can be used to configure a cloud computing system. For example, a server system can determine, using a first machine learning model, a first selection of a first parameter for configuring a distributed computing system based on a first input provided by a user in a natural language format. The server system can determine, using a second machine learning model, an additional set of parameters for the distributed computing system. The server system can output the additional set of parameters for display to the user in the natural language format. The server system can determine, using the first machine learning model, a second selection of a second parameter of the additional set of parameters based on a second input provided by the user. The server system can automatically deploy the distributed computing system configured with the first parameter and the second parameter.

Abstract: A search query can be resolved efficiently using stored key information in a data cache. A system can receive a search query via an application programming interface (API). The search query can comprise a set of query parameters. The system can select at least one interaction event search service. The system can identify a plurality of interaction events relevant to the search query. Each interaction event can include a description and key information used to locate each interaction event. The system can retrieve the key information for each interaction event. The system can store, in a data cache, the key information for each interaction event. Subsequent to retrieving the key information, the system can retrieve, using at least a portion of the key information, descriptions of a first portion of interaction events. Additionally, the system can transmit the descriptions of the first portion of interaction events to the user.

Abstract: Some examples can automatically detect functional anomalies associated with software services of a distributed application. For example, a system can receive a configuration file indicating an execution sequence for a set of services of a distributed application. The configuration file can include a mapping of a set of endpoint addresses to the set of services. The system can also receive a mixed set of debugging data associated with the set of services. The system can then parse the mixed set of debugging data into groups corresponding to the set of services based on the mapping in the configuration file. The system can then determine a sequence of debugging events by analyzing the groups, detect a functional anomaly associated with the set of services based on the sequence of debugging events, and generate an alert indicating the functional anomaly to a user.

Abstract: A search query can be resolved efficiently using stored key information in a data cache. A system can receive a search query via an application programming interface (API). The search query can comprise a set of query parameters. The system can select at least one interaction event search service. The system can identify a plurality of interaction events relevant to the search query. Each interaction event can include a description and key information used to locate each interaction event. The system can retrieve the key information for each interaction event. The system can store, in a data cache, the key information for each interaction event. Subsequent to retrieving the key information, the system can retrieve, using at least a portion of the key information, descriptions of a first portion of interaction events. Additionally, the system can transmit the descriptions of the first portion of interaction events to the user.