Abstract: A modular machine learning-as-a-service (MLAAS) system uses machine learning to respond to tasks without requiring machine learning modeling or design knowledge by its users. The MLAAS system receives an inference request including a model identifier and a target defining features for use in processing the inference request. The features correspond to a task for evaluation using a machine learning model associated with the model identifier. An inference outcome is generated by processing the inference request using the target as input to the model. Feedback indicating an accuracy of the inference outcome with respect to the task is later received and used to generate a training data set, which the MLAAS can use to further train model used to generate the inference outcome. As a result, the training of a machine learning model by the MLAAS system is limited to using data resulting from an inference performed using that model.

Abstract: A modular machine learning-as-a-service (MLAAS) system uses machine learning to respond to tasks without requiring machine learning modeling or design knowledge by its users. The MLAAS system receives an inference request including a model identifier and a target defining features for use in processing the inference request. The features correspond to a task for evaluation using a machine learning model associated with the model identifier. An inference outcome is generated by processing the inference request using the target as input to the model. Feedback indicating an accuracy of the inference outcome with respect to the task is later received and used to generate a training data set, which the MLAAS can use to further train model used to generate the inference outcome. As a result, the training of a machine learning model by the MLAAS system is limited to using data resulting from an inference performed using that model.

Abstract: Methods are provided that allow global access to redox-based molecular information by coupling electrochemical measurements with signal processing approaches. More specifically, the disclosure provides methods that rely on the use of redox probes to assay samples for redox activities that act to exchange electrons with the probe thereby generating detectable optical and electrochemical signature signals that can then be assigned to a sample feature of interest. In particular embodiments, the disclosed assay methods are useful for diagnosis and prognosis of disorders, such as schizophrenia, that are found to be associated with a specific redox-based signature within a subject sample.