Abstract: Methods and systems are provided for the deployment of machine learning based processes to public clouds. For example, a method for deploying a machine learning based process may include developing and training the machine learning based process to perform an activity, performing at least one of identifying and receiving an identification of a set of one or more public clouds that comply with a set of regulatory criteria used to regulate the activity, selecting a first public cloud of the set of one or more public clouds that complies with the set of regulatory criteria used to regulate the activity, and deploying the machine learning based process to the first public cloud of the set of one or more public clouds.

Abstract: Example implementations relate to locating cloud system resources. A system can comprise a processor and a non-transitory memory resource storing machine readable instructions to receive a global positioning system (GPS) coordinate for hardware resources positioned at a plurality of geographic locations, tag resources stored at each of the plurality of geographic locations with a corresponding GPS coordinate, and determine a corresponding geographic location of a portion of the tagged resources based on extracted GPS coordinates for the portion of the tagged resources.

Abstract: A technique includes providing, by a computer, a portal to provide a catalog describing features of a plurality of cloud services available and to be provided by otherwise unused hardware resources of a plurality of private cloud providers. The technique includes, by the computer, in response to input received through the portal selecting a given cloud service for a tenant, exposing the given cloud service to the tenant. Exposing the given cloud service to the user includes requesting a given cloud provider corresponding to the given cloud service to configure the given cloud provider to provide the given cloud service to the tenant.

Abstract: Some examples relate to collection of performance metrics from a device. In an example, performance metrics for collection from a first device may be selected. The performance metrics may be indexed by assigning an index entry to respective performance metrics on the first device. A fixed sequence of the performance metrics may be maintained on first device. The fixed sequence of the performance metrics along with the index entry assigned to the respective performance metrics may be shared with a second device. A first performance data of the respective performance metrics on first device may be determined. The first performance data of the respective performance metrics may be shared with second device. The sharing may comprise sending, to second device, the index entry and the first performance data of the respective performance metrics in an order corresponding to the fixed sequence of the performance metrics on first device.

Abstract: Some examples relate to collection of performance metrics from a device. In an example, performance metrics for collection from a first device may be selected. The performance metrics may be indexed by assigning an index entry to respective performance metrics on the first device. A fixed sequence of the performance metrics may be maintained on first device. The fixed sequence of the performance metrics along with the index entry assigned to the respective performance metrics may be shared with a second device. A first performance data of the respective performance metrics on first device may be determined. The first performance data of the respective performance metrics may be shared with second device. The sharing may comprise sending, to second device, the index entry and the first performance data of the respective performance metrics in an order corresponding to the fixed sequence of the performance metrics on first device.

Abstract: Example implementations relate to locating cloud system resources. A system can comprise a processor and a non-transitory memory resource storing machine readable instructions to receive a global positioning system (GPS) coordinate for hardware resources positioned at a plurality of geographic locations, tag resources stored at each of the plurality of geographic locations with a corresponding GPS coordinate, and determine a corresponding geographic location of a portion of the tagged resources based on extracted GPS coordinates for the portion of the tagged resources.

Abstract: Methods and systems are provided for the deployment of machine learning based processes to public clouds. For example, a method for deploying a machine learning based process may include developing and training the machine learning based process to perform an activity, performing at least one of identifying and receiving an identification of a set of one or more public clouds that comply with a set of regulatory criteria used to regulate the activity, selecting a first public cloud of the set of one or more public clouds that complies with the set of regulatory criteria used to regulate the activity, and deploying the machine learning based process to the first public cloud of the set of one or more public clouds.

Abstract: A technique includes providing, by a computer, a portal to provide a catalog describing features of a plurality of cloud services available and to be provided by otherwise unused hardware resources of a plurality of private cloud providers. The technique includes, by the computer, in response to input received through the portal selecting a given cloud service for a tenant, exposing the given cloud service to the tenant. Exposing the given cloud service to the user includes requesting a given cloud provider corresponding to the given cloud service to configure the given cloud provider to provide the given cloud service to the tenant.

Abstract: Methods and systems are provided for presenting operating information for an aircraft in a vertical profile displayed on a display device associated with the aircraft. The vertical profile graphically depicts at least a portion of a flight plan for the aircraft, wherein the portion of the flight plan comprises a plurality of reference points. The method comprises calculating, for each reference point of the plurality of reference points, a predicted value of a first operating parameter for the aircraft at the respective reference point based at least in part on current status information for the aircraft, resulting in predicted values for the first operating parameter, and displaying the predicted values for the first operating parameter in the vertical profile.

Abstract: A system for producing maximal, but reversible bioeffects, for neuromuscular disruption. Methods for facilitation of neuromuscular functions in diseases and conditions of decreased excitability are provided.

Abstract: Methods and systems are provided for presenting operating information for an aircraft in a vertical profile displayed on a display device associated with the aircraft. The vertical profile graphically depicts at least a portion of a flight plan for the aircraft, wherein the portion of the flight plan comprises a plurality of reference points. The method comprises calculating, for each reference point of the plurality of reference points, a predicted value of a first operating parameter for the aircraft at the respective reference point based at least in part on current status information for the aircraft, resulting in predicted values for the first operating parameter, and displaying the predicted values for the first operating parameter in the vertical profile.

Abstract: A system for producing maximal, but reversible bioeffects, for neuromuscular disruption. Methods for facilitation of neuromuscular functions in diseases and conditions of decreased excitability are provided.