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.