Hardware-aware automated machine learning (automl) model creation and optimization

Abstract

Automated machine learning (Auto ML) for creating and optimizing ML models using a model store for storing: trained ML models and hardware models; test metrics data corresponding to the stored models; ML advised-models. Using a model meta-services (MMS) for: accessing the stored models and the test metrics data; creating the ML meta-models based on the runtime test metrics data; and answering MPC queries. Using a models producer and consumer (MPC) for: selecting a ML advised-model; testing the selected ML advised-model using selected ML test inputs and outputs to provide runtime test metrics data; optimizing the selected ML advised-model using the runtime test metrics data; sending the optimized ML advised-model to the model store unit for storing as one of the stored ML advised-models; and sending the runtime test metrics data to the model store unit for storing as part of the runtime test metrics data; and sending the MPC queries.