Abstract: Methods, apparatus, and processor-readable storage media for automatically summarizing event-related data using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining text-based data and non-text-based data associated with at least one virtual event comprising one or more participants; generating a content-related summarization of one or more of at least a portion of the text-based data and at least a portion of the non-text-based data using at least a first set of one or more artificial intelligence techniques; generating a participant sentiment-related summarization associated with one or more of at least a portion of the text-based data and at least a portion of the non-text-based data using at least a second set of one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on one or more of the content-related summarization and the participant sentiment-related summarization.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes, by a virtual event assistant, joining an online meeting and receiving a content of the online meeting in real-time, the content including an audio stream and a video stream of the online meeting. The method also includes, by the virtual event assistant, generating a summarized content of the online meeting based on a transcript of the online meeting, wherein generating the summarized content includes applying one or more artificial intelligence (AI)-based techniques to the transcript of the online meeting. The method further includes, by the virtual meeting assistant, determining contextual metadata of the online meeting based on analysis of the content of the online meeting. The method may also include providing the summarized content with the contextual metadata of the online meeting for playback by a user, for example.

Abstract: In one aspect, an example methodology implementing the disclosed techniques includes receiving information regarding a customer to onboard to a managed service and determining one or more relevant features from the information regarding the customer, the one or more relevant features correlated with historical onboarding times. The method also includes determining, using a machine learning (ML) model, an expected time to onboard the customer to the managed service based on the one or more relevant features.

Abstract: In some examples, a server may receive, from a software agent, data associated with a particular component of a plurality of components in a system (e.g., an information technology (IT) network). The data may include connection data, network location data, software data, user data, hardware data, and network routing data. The server may determine, using a k-nearest classification algorithm and based on the data, a current usage of the particular component. The server may determine an intended usage of the particular component and perform a comparison of the current usage with the intended usage. If the server determines that the current usage differs from the intended usage by at least a predetermined percentage, then the server may perform one or more remediation actions to modify the current usage to differ from the intended usage by less than the predetermined percentage.

Abstract: In some examples, a server may receive, from a software agent, data associated with a particular component of a plurality of components in a system (e.g., an information technology (IT) network). The data may include connection data, network location data, software data, user data, hardware data, and network routing data. The server may determine, using a k-nearest classification algorithm and based on the data, a current usage of the particular component. The server may determine an intended usage of the particular component and perform a comparison of the current usage with the intended usage. If the server determines that the current usage differs from the intended usage by at least a predetermined percentage, then the server may perform one or more remediation actions to modify the current usage to differ from the intended usage by less than the predetermined percentage.