Abstract: A wireless initiator access point comprising a communication interface with an array of antennas and configured to operate the array of antennas with a precoding configuration for multi-user multiple-input multiple-output (MU-MIMO) communication with a plurality of wireless stations; send a Null Data Packet (NDP) Announcement frame to the wireless stations; trigger a wireless participator access point to send an NDP frame to the wireless stations; receive a plurality of Compressed Beamforming frames from the wireless stations; and forward the Compressed Beamforming frames to the wireless participator access point.

Abstract: A cloud hosting environment configuration interface is provided. Cloud-based services defined in the hosting environment are configured through cloud-based resources using the interface. Previously defined services and their resources can be recalled by reference within the interface and properties and settings for those resources custom modified as needed for a new hosted service. Cloud resources can be added and removed via the interface. In an embodiment, the interface is a Command Line Interface (CLI).

Abstract: A cloud hosting environment configuration interface is provided. Cloud-based services defined in the hosting environment are configured through cloud-based resources using the interface. Previously defined services and their resources can be recalled by reference within the interface and properties and settings for those resources custom modified as needed for a new hosted service. Cloud resources can be added and removed via the interface. In an embodiment, the interface is a Command Line Interface (CLI).

Abstract: Machine-Learning Model (MLM) build technique is provided. Build instructions are normalized into a cloud-independent format. Each cloud identified in the instructions is assigned a specific adapter. The adapter translates the normalized format into a cloud-specific format and the adapters interact with configuration Application Programming Interfaces (APIs) of the specific cloud to process the instructions in the cloud-specific format. As the adapters interact with the corresponding APIs, output feedback from the APIs is live streamed within an interface to a developer that provided the instructions for monitoring the builds simultaneously being configured on multiple clouds. When the build instructions complete with the APIs on the clouds, the user is presented an option to initiate an orchestrator that loads, initiates, and trains the MLM as a unique instance of the MLM on each cloud.

Abstract: Machine-Learning Model (MLM) build technique is provided. Build instructions are normalized into a cloud-independent format. Each cloud identified in the instructions is assigned a specific adapter. The adapter translates the normalized format into a cloud-specific format and the adapters interact with configuration Application Programming Interfaces (APIs) of the specific cloud to process the instructions in the cloud-specific format. As the adapters interact with the corresponding APIs, output feedback from the APIs is live streamed within an interface to a developer that provided the instructions for monitoring the builds simultaneously being configured on multiple clouds. When the build instructions complete with the APIs on the clouds, the user is presented an option to initiate an orchestrator that loads, initiates, and trains the MLM as a unique instance of the MLM on each cloud.