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.

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.

Abstract: Systems and methods for optimizing trained ML hardware models by collecting machine learning (ML) training inputs and outputs; selecting a ML model architecture from ML model architectures; training the selected ML model architecture with the ML training inputs and outputs; selecting a hardware processor from hardware processors; and creating a trained ML hardware model by inputting the selected hardware processor with the trained ML model. ML test inputs and outputs, and types of test metrics are selected and used to test the trained ML hardware model to provide runtime test metrics data for ML output predictions made by the trained ML hardware model. The trained ML hardware model is optimized to become an optimized trained ML hardware model using the runtime test metrics by selecting a new selected ML model architecture, selecting a new selected hardware processor, or updating the trained ML model using the runtime metrics test data.