Sap Se

Rahul Choudhary

Rajendra Kumar

Seshadri Chatterjee

Naga Sai Narasimha Guru Charan Koduri

Priyanshu Shukla

A machine learning model including an autoencoder may be trained based on training data that includes sequences of non-anomalous performance metrics from an information technology system but excludes sequences of anomalous performance metrics. The trained machine learning model may process a sequence of performance metrics from the information technology system by generating an encoded representation of the sequence of performance metrics and generating, based on the encoded representation, a reconstruction of the sequence of performance metrics. An occurrence of the anomaly at the information technology system may be detected based on a reconstruction error present in reconstruction of the sequence of performance metrics. Related systems, methods, and articles of manufacture are provided.

Variational autoencoding for anomaly detection

Disclosed herein are system, method, and computer program product embodiments for developing natural language skills automatically. In order to generate a skill, metadata is read from a connected service, wherein the metadata specifies entities provided by the service. A relevant entity of the entities provided by the service is determined, and a likely request on the entity is generated.

Natural language skill generation for digital assistants

The present disclosure involves systems, software, and computer implemented methods for automatic view generation based on annotations. One example method includes receiving a request to display a user interface view on a client device. Metadata that defines at least one entity included in at least one data source is received. Annotations that define user interface elements for displaying information for the at least one entity are received. A metamodel is generated using the received metadata and the received annotations. Native user interface elements are automatically generated using the metamodel. The native user interface elements are native to the client device. The native user interface elements in the user interface view on the client device.

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Bengaluru, India

Rahul Choudhary