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 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: In one embodiment, data received from one or more streaming data sources may be monitored by one or more devices. A rate of change in flow of the data received from the one or more streaming data sources may be ascertained. It may be determined whether the rate of change in flow of the data received from the one or more streaming data sources exceeds a threshold rate. Transmission of an alert may be initiated according to a result of determining whether the rate of change in the flow of the data received from the one or more streaming data sources exceeds the threshold rate.

Abstract: In one embodiment, data received from one or more streaming data sources may be monitored by one or more devices. A rate of change in flow of the data received from the one or more streaming data sources may be ascertained. It may be determined whether the rate of change in flow of the data received from the one or more streaming data sources exceeds a threshold rate. Transmission of an alert may be initiated according to a result of determining whether the rate of change in the flow of the data received from the one or more streaming data sources exceeds the threshold rate.