Hardware-aware machine learning model search mechanisms

One or more non-transitory computer readable media (NTCRM) comprising instructions for operating a machine learning architecture search interface (MLASI), wherein execution of the instructions by one or more processors of a compute node is to cause the compute node to:

• serve the MLASI to a client device;
• and
• in response to receipt of an individual machine learning (ML) configuration from the client device via the MLASI:
• determine a set of ML tasks and a set of datasets indicated by the individual ML configuration,
• generate a task list from the set of ML tasks,
• identify individual datasets of the set of datasets to be generalized datasets or task-specific datasets, the task-specific datasets being datasets that can only be used for one task in the task list and the generalized datasets being datasets that can be used for more than one task in the task list,
• generate one or more data batches from individual datasets based on the task-specific datasets and the generalized datasets,
• determine a performance metric of each task in the task list,
• generate a combined performance metric (CPM) based on the determined performance metric of each task in the task list,
• determine a set of generalized machine learning architectures (GMLAs) and corresponding performance metrics based on optimization of a multi-objective function, wherein each GMLA of the set of GMLAs is a machine learning (ML) architecture that can be used for more than one ML task and using more than one type of dataset, and wherein the set of GMLAs comprises a set of Pareto optimal solutions of the multi-objective function, and
• indicate, via the MLASI, the set of GMLAs and the corresponding performance metrics for deployment on a hardware platform.