Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a feature configuration for a feature. Next, the system obtains, from the feature configuration, an anchor containing metadata for accessing the feature in an environment. The system then uses one or more attributes of the anchor to retrieve one or more feature values of the feature from the environment. Finally, the system provides the one or more feature values for use with one or more machine-learning models.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system selects a set of entity keys associated with reference feature values used with one or more machine learning models, wherein the reference feature values are generated in a first environment. Next, the system matches the set of entity keys to feature values from a second environment. The system then compares the feature values and the reference feature values to assess a consistency of a feature across the first and second environments. Finally, the system outputs a result of the assessed consistency for use in managing the feature in the first and second environments.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains feature configurations for a set of features and a command for inspecting a data set that is produced using the feature configurations. Next, the system obtains, from the feature configurations, one or more anchors containing metadata for accessing the set of features in an environment and a join configuration for joining a feature with one or more additional features. The system then uses the anchors to retrieve feature values of the features and zips the feature values according to the join configuration without matching entity keys associated with the feature values. Finally, the system outputs the zipped feature values in response to the command.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a feature configuration for a feature. Next, the system obtains, from the feature configuration, an anchor containing metadata for accessing the feature in an environment. The system then uses one or more attributes of the anchor to retrieve one or more feature values of the feature from the environment. Finally, the system provides the one or more feature values for use with one or more machine-learning models.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains feature configurations for a set of features. Next, the system obtains, from the feature configurations, an anchor containing metadata for accessing a first feature in an environment and a feature derivation for generating a second feature from the first feature. The system then uses the anchor to retrieve feature values of the first feature from the environment and uses the feature derivation to generate additional feature values of the second feature from the feature values of the first feature. Finally, the system provides the additional feature values for use with one or more machine learning models.

Abstract: The disclosed embodiments provide a system for sharing features in a feature management framework. During operation, the system creates a repository of feature configurations for a set of features that are accessed across multiple environments. Next, the system identifies dependencies of the repository. The system then copies shared feature configurations from other repositories represented by the dependencies. Finally, the system combines the shared feature configurations with existing feature configurations in the repository for use in retrieving feature values for one or more machine learning models.

Abstract: Among other things, embodiments of the present disclosure discussed herein may be used to identify and classify new web page content based on metadata in the content. The embodiments of the present disclosure help provide a classification solution for web content that is scalable to large numbers of content attributes, has the flexibility to predict values for a wide range of diverse attributes, and provides consistent and highly accurate results.