Abstract: In some implementations, a device may receive input data for machine learning model training. The device may generate, based on the input data, artificial data using one or more machine learning models operating on one or more servers, wherein the one or more machine learning models includes a generative artificial intelligence model configured to generate the artificial data such that the artificial data shares a set of common characteristics with the input data. The device may train, using the artificial data and metadata associated with the artificial data, a particular machine learning model. The device may transmit an output associated with the particular machine learning model.

Abstract: In some implementations, a device may receive a request for a machine learning prediction. The device may access a feature store that stores data associated with a plurality of possible machine learning model features, wherein accessing the feature store comprises performing a remote procedure call (RPC) to generate a new data object at the feature store, the new data object having the device subscribed as a client. The device may receive, from the feature store in connection with the device being subscribed to the new data object as the client, a set of values associated with a composite feature. The device may execute a machine learning model using the set of values associated with the composite feature. The device may output a machine learning prediction based on a result of executing the machine learning model using the set of values associated with the composite feature.

Abstract: In some implementations, a device may receive an input for a first prediction model. The device may execute, using the input, the first prediction model to generate a set of outputs, wherein the set of outputs is based on a set of inputs to a data processing pipeline associated with the first prediction model. The device may generate using a simulation engine and based on the set of outputs of the first prediction, a set of simulations of a set of results of implementing a set of actions associated with the first prediction model, wherein the set of simulations is associated with a simulated dataset representing a set of forecasts for simulating the set of results of implementing the set of actions. The device may output the simulated dataset to a model generation pipeline.

Abstract: Systems and methods for a novel architecture to support always-on applications and/or models suffering drift in their results. The system may comprise one or more servers that are configured to track the historical behavior of incoming request data for a model and/or redirect the request as needed using parallel data domains. The one or more servers may maintain and update a catalog of potential data domains that partitions the historically received data. One partition may comprise data output from a current model. Another partition may comprise detected outliers in the data. In the case of drift, outliers, and/or anomalies in the incoming data, the system may return an error signal that causes data to be duplicated into a new data domain.