Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for automatically generating and executing machine learning pipelines based on a variety of user selections of various settings, machine learning structures, and other machine learning pipeline criteria. In particular, in one or more embodiments, the disclosed systems utilize user input selecting various machine learning pipeline settings to generate machine learning model pipeline files. Further, the disclosed systems execute and deploy the machine learning pipelines based on user-selected schedules. In some embodiments, the disclosed systems also register the machine learning pipelines and associated machine learning pipeline data in a machine learning pipeline registry. Further, the disclosed systems can generate and provide a machine learning pipeline graphical user interface for monitoring and managing machine learning pipelines.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a low-latency machine learning model prediction cache for improving distribution of current state machine learning predictions across computer networks. In particular, in one or more implementations, the disclosed systems utilize a prediction registration platform for defining prediction datatypes and corresponding machine learning model prediction templates. Moreover, in one or more embodiments, the disclosed systems generate a machine learning data repository that includes predictions generated from input features utilizing machine learning models. From this repository, the disclosed systems also generate a low-latency machine learning prediction cache by extracting current state machine learning model predictions according to the machine learning prediction templates and then utilize the low-latency machine learning prediction cache to respond to queries for machine learning model predictions.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a check-return machine-learning model to predict whether a mobile check deposit will result in a check-return (e.g., due to mobile check deposit fraud). For instance, the disclosed systems can receive a request to initiate a mobile check deposit. In response to the request, the disclosed systems identify one or more features associated with the mobile check deposit. For example, the one or more features may include check features, historical returned and posted checks for a check maker account, recipient account historical data, or recipient account payment schedule data, etc. From the one or more features, the check-return machine-learning model generates a check-return prediction. In turn, the disclosed systems utilize the check-return prediction to process the mobile check deposit.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement a pre-defined model container workflow allowing computing devices to flexibly and efficiently define, train, deploy, and maintain machine-learning models. For instance, the disclosed systems can provide scaffolding and boilerplate code for machine-learning models. To illustrate, boilerplate code can include predetermined designs of base classes for common use cases like training, batch inference, etc. In addition, the scaffolding provides an opinionated directory structure for organizing code of a machine-learning model. Further, the disclosed systems can provide containerization and various tooling (e.g., command interface tooling, platform upgrade tooling, and model repository management tooling). Additionally, the disclosed systems can provide out of the box compatibility with one or more different compute instances for increased flexibility and cross-system integration.

Abstract: This disclosure describes a feature family system that, as part of an inter-network facilitation system, can intelligently generate and maintain a feature family repository for quickly and efficiently retrieving and providing machine learning features upon request. For example, the disclosed systems can generate a feature family repository as a centralized network location of feature references indicating network locations where different machine learning features are stored. In some cases, the disclosed systems identify a stored feature family that matches the request and retrieves the stored features from their respective network locations. The disclosed systems can generate feature families for online features as well as offline features and can automatically update feature values associated with various machine learning features on a period basis or in response to trigger events.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.

Abstract: A computer-implemented method for adaptive display brightness adjustment, the method comprising: obtaining current state data characterizing a current state of a device having a display with an adjustable brightness; providing the current state data as input to a brightness prediction machine learning model, wherein the model is configured to process the current state data in accordance with current values of a set of model parameters to generate as output a proposed display brightness for the display of the device; setting the brightness of the display to a brightness that is lower than the proposed display brightness in accordance with an exploration policy; determining whether a user of the device manually adjusts the display brightness; and in response to determining that the user did not manually adjust the display brightness, using the lower brightness as a target output for adjusting the current values of the set of model parameters.