Abstract: Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a stateful feature, the feature management platform can receive a processing artifact from a computing device. The processing artifact defines the stateful feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job (e.g., the API defines a pipeline), which when initiated generates a vector that encapsulates the stateful feature. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction that is transmitted to the feature management platform. Subsequently, the predication and stateful feature can be transmitted to other computing devices.

Abstract: Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a feature the feature management platform can receive a processing artifact (e.g., a configuration file and code fragment) from a computing device. The processing artifact defines the feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job, which when initiated generates a vector that encapsulates the feature data. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction. The prediction is transmitted to the feature management platform and can be transmitted to other computing devices, upon request.

Abstract: A method for generating a synthetic dataset involves generating discretized synthetic data based on driving a model of a cumulative distribution function (CDF) with random numbers. The CDF is based on a source dataset. The method further includes generating the synthetic dataset from the discretized synthetic data by selecting, for inclusion into the synthetic dataset, values from a multitude of entries of the source dataset, based on the discretized synthetic data, and providing the synthetic dataset to a downstream application that is configured to operate on the source dataset.

Abstract: A method including extracting, from an input, supported data. The input includes outputs from machine learning models in different formats. The supported data includes a subset of the input after data normalization. The method also includes inferring, from the supported data, data types to be used with respect to generating metrics for the machine learning models. The method also includes generating, from the supported data and using the data types, a relational event including the supported data. The relational event further includes a first data structure object including the types and having a first data structure different than the different formats. The method also includes calculating, using the supported data in the first data structure, the metrics for the machine learning models. The method also includes generating, from the relational event, a monitoring event. The monitoring event includes a second data structure object segmented into data buckets storing the metrics.

Abstract: Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a stateful feature, the feature management platform can receive a processing artifact from a computing device. The processing artifact defines the stateful feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job (e.g., the API defines a pipeline), which when initiated generates a vector that encapsulates the stateful feature. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction that is transmitted to the feature management platform. Subsequently, the predication and stateful feature can be transmitted to other computing devices.

Abstract: Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a stateful feature, the feature management platform can receive a processing artifact from a computing device. The processing artifact defines the stateful feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job (e.g., the API defines a pipeline), which when initiated generates a vector that encapsulates the stateful feature. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction that is transmitted to the feature management platform. Subsequently, the predication and stateful feature can be transmitted to other computing devices.

Abstract: Certain aspects of the present disclosure provide techniques for a feature management platform to asynchronously implement an ensemble of machine learning models (or AI/ML models). The feature management platform can transmit feature data presently available on the feature queue (e.g., published on the feature queue or retrieved from a persistent data layer). The feature management platform can transmit the feature data to a first group of the machine learning models, capable of inputting the presently available feature data to generate predictions. The predictions can be transmitted back to the feature queue to consume—as well as store in the persistent data layer. The predictions, as well as any newly generated feature data, can be provided to the remaining machine learning models in the ensemble. The prediction of the ensemble can then provided to a consumer (e.g., an organization).

Abstract: Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a stateful feature, the feature management platform can receive a processing artifact from a computing device. The processing artifact defines the stateful feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job (e.g., the API defines a pipeline), which when initiated generates a vector that encapsulates the stateful feature. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction that is transmitted to the feature management platform. Subsequently, the predication and stateful feature can be transmitted to other computing devices.

Abstract: Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a feature the feature management platform can receive a processing artifact (e.g., a configuration file and code fragment) from a computing device. The processing artifact defines the feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job, which when initiated generates a vector that encapsulates the feature data. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction. The prediction is transmitted to the feature management platform and can be transmitted to other computing devices, upon request.

Abstract: A method including extracting, from an input, supported data. The input includes outputs from machine learning models in different formats. The supported data includes a subset of the input after data normalization. The method also includes inferring, from the supported data, data types to be used with respect to generating metrics for the machine learning models. The method also includes generating, from the supported data and using the data types, a relational event including the supported data. The relational event further includes a first data structure object including the types and having a first data structure different than the different formats. The method also includes calculating, using the supported data in the first data structure, the metrics for the machine learning models. The method also includes generating, from the relational event, a monitoring event. The monitoring event includes a second data structure object segmented into data buckets storing the metrics.

Abstract: A method for generating a synthetic dataset involves generating discretized synthetic data based on driving a model of a cumulative distribution function (CDF) with random numbers. The CDF is based on a source dataset. The method further includes generating the synthetic dataset from the discretized synthetic data by selecting, for inclusion into the synthetic dataset, values from a multitude of entries of the source dataset, based on the discretized synthetic data, and providing the synthetic dataset to a downstream application that is configured to operate on the source dataset.