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.