HITACHI VANTARA LLC

Example implementations described herein are directed to systems and methods for generation and deployment of automated and autonomous self-learning machine learning models, which can include generating a predictive model and a prescriptive model through an offline learning process at a first system; controlling operations of a second system through deploying the predictive model and the prescriptive model to the second system; and autonomously updating the predictive model and the prescriptive model from feedback from the second system through an online learning process while the prescriptive model and the predictive model are deployed on the second system.

Model drift management of one or more machine learning models deployed across one or more physical systems, including executing a first process configured to detect model drift occurring on the one or more deployed machine learning models in real time, the first process configured to intake time series sensor data of one or more physical systems and one or more labels associated with the time series sensor data to output detected model drift detected from the one or more deployed machine learning models; and executing a second process configured to predict model drift from the one or more deployed machine learning models, the second process configured to intake the output model drifts from the first machine learning model and the time series sensor data to output predicted model drift of the one or more deployed machine learning models, wherein the second process is another machine learning model.

Example implementations described herein are directed to a system with a plurality of sensors associated with a plurality of apparatuses, which involves, for receipt of a hypothesis canvas and meta-data from the plurality of sensors, generating a plurality of meta-pipeline stages, the plurality of meta-pipeline stages having the meta-data and one or more stages, each of the one or more stages being an analytics application or a machine learning application stacked in execution order based on the meta-data and the hypothesis canvas; and executing the plurality of meta-pipeline stages in execution order on the meta-data to generate an analytics model for the system.

Example implementations described herein are directed to a system with a plurality of sensors associated with a plurality of apparatuses, which involves, for receipt of a hypothesis canvas and meta-data from the plurality of sensors, generating a plurality of meta-pipeline stages, the plurality of meta-pipeline stages having the meta-data and one or more stages, each of the one or more stages being an analytics application or a machine learning application stacked in execution order based on the meta-data and the hypothesis canvas; and executing the plurality of meta-pipeline stages in execution order on the meta-data to generate an analytics model for the system.

Example implementations described herein are directed to a novel Automated Machine Learning (AutoML) framework that is generated on an AutoML library so as to facilitate functionality to incorporate multiple machine learning model libraries within the same framework through a solution configuration file. The example implementations further involve a solution generator that identifies solution candidates and parameters for machine learning models to be applied to a dataset specified by the solution configuration file.

Example implementations described herein are directed to systems and methods for generation and deployment of automated and autonomous self-learning machine learning models, which can include generating a predictive model and a prescriptive model through an offline learning process at a first system; controlling operations of a second system through deploying the predictive model and the prescriptive model to the second system; and autonomously updating the predictive model and the prescriptive model from feedback from the second system through an online learning process while the prescriptive model and the predictive model are deployed on the second system.

Example implementations described herein are directed to a novel Automated Machine Learning (AutoML) framework that is generated on an AutoML library so as to facilitate functionality to incorporate multiple machine learning model libraries within the same framework through a solution configuration file. The example implementations further involve a solution generator that identifies solution candidates and parameters for machine learning models to be applied to a dataset specified by the solution configuration file.