Abstract: Systems and methods are presented to provide a first machine learning model to a collaboration platform. The systems and methods receive a second machine learning model from the collaboration platform that indicates the second machine learning model is based on the first machine learning model. The systems and methods test the second machine learning model using criteria corresponding to the first machine learning model to determine whether the second machine learning model is valid. In turn, the systems and methods publish the second machine learning model to a repository in response to determining that the second machine learning model is valid.

Abstract: Energy consumption associated with users of a distributed computing system can be monitored using tracing according to some examples described herein. In one such example, a system can execute tracing software configured to track usage of a software service and generate tracing information. The tracing information cant attribute the usage of the software service to a group of users. The system can analyze the tracing information to determine a portion of the usage to attribute to a given user. The system can then determine an energy consumption estimate for the user, for example based on the portion of the usage associated with the user and an energy consumption amount associated with the software service. A graphical user interface can be generated indicating the energy consumption estimate for the user.

Abstract: Energy consumption associated with users of a distributed computing system can be monitored using tracing according to some examples described herein. In one such example, a system can execute tracing software configured to track usage of a software service and generate tracing information. The tracing information cant attribute the usage of the software service to a group of users. The system can analyze the tracing information to determine a portion of the usage to attribute to a given user. The system can then determine an energy consumption estimate for the user, for example based on the portion of the usage associated with the user and an energy consumption amount associated with the software service. A graphical user interface can be generated indicating the energy consumption estimate for the user.

Abstract: An enterprise level Application Programming Interface (API) driven service to integrate and automate data quality processes within organizations is provided. The service has capabilities to ingest data from heterogenous data sources and provide data quality metrics irrespective of data domain type. A subscribing application can write Data Quality (DQ) checks as code using templates of a customized rule engine and generate metrics based on pre-defined schedules. Analytics and visualization and reporting capabilities of the service empower stakeholders such as data managers, application developers, and risk assessors to obtain actionable insights into data quality. Stakeholders can use service-generated DQ measures, such as accuracy, completeness, and timeliness, to identify data quality concerns and proactively implement remediation actions to reduce data use risk.

Abstract: An enterprise level Application Programming Interface (API) driven service to integrate and automate data quality processes within organizations is provided. The service has capabilities to ingest data from heterogenous data sources and provide data quality metrics irrespective of data domain type. A subscribing application can write Data Quality (DQ) checks as code using templates of a customized rule engine and generate metrics based on pre-defined schedules. Analytics and visualization and reporting capabilities of the service empower stakeholders such as data managers, application developers, and risk assessors to obtain actionable insights into data quality. Stakeholders can use service-generated DQ measures, such as accuracy, completeness, and timeliness, to identify data quality concerns and proactively implement remediation actions to reduce data use risk.

Abstract: Energy consumption associated with users of a distributed computing system can be monitored using tracing according to some examples described herein. In one such example, a system can execute tracing software configured to track usage of a software service and generate tracing information. The tracing information cant attribute the usage of the software service to a group of users. The system can analyze the tracing information to determine a portion of the usage to attribute to a given user. The system can then determine an energy consumption estimate for the user, for example based on the portion of the usage associated with the user and an energy consumption amount associated with the software service. A graphical user interface can be generated indicating the energy consumption estimate for the user.

Abstract: Automated quantum circuit job submission and status determination is disclosed. A classical computing system receives a request to execute a quantum circuit that includes quantum program instructions that perform one or more quantum operations on one or more qubits. The classical computing system requests, from a first quantum computing system that includes a first set of quantum computing devices, metadata describing characteristics of the first set of quantum computing devices. The classical computing system determines, based on the quantum circuit and the metadata, a selected quantum computing device of the first set of quantum computing devices, and sends, to the first quantum computing system, quantum circuit information that identifies the quantum circuit and instructions to execute the quantum circuit on the selected quantum computing device.

Abstract: Systems, methods, and non-transitory computer readable media can receive a request from a user to access information associated with an entity through an interface supported on a particular surface. A plurality of candidate content items of a content type associated with the entity can be ranked for the user and the particular surface based on a machine learning model. At least one of the ranked plurality of candidate content items of the content type can be provided for display through the interface.