Abstract: The present disclosure provides an application development platform and associated software development kits (“SDKs”) that provide comprehensive services for generation, deployment, and management of machine-learned models used by computer applications such as, for example, mobile applications executed by a mobile computing device. In particular, the application development platform and SDKs can provide or otherwise leverage a unified, cross-platform application programming interface (“API”) that enables access to all of the different machine learning services needed for full machine learning functionality within the application. In such fashion, developers can have access to a single SDK for all machine learning services.

Abstract: A system and method for orienting the presentation of a virtual environment with respect to multiple users in a shared virtual space is provided. The multiple users may be physically present in different physical spaces. For each of the multiple users, the system may detect physical constraints associated with the respective physical space, and may determine a longest, unobstructed physical path in the physical space based on an orientation of the user in the physical space and the associated physical constraints. A presentation of the virtual environment to the multiple users in the shared virtual space may then be oriented with respect to each of the multiple users so as to maximize interaction amongst the multiple users in the shared virtual space.

Abstract: A developer/requestor may select a subset of users for a promotion by querying a database; however, the developer may not be provided with specific details about each user (e.g., purchase history, browsing history, etc.). The system returns a response to the query that obfuscates a user's data while simultaneously providing the developer with a response to indicate to the developer whether or not a user or group of users is promotion eligible based on the developer's criteria. The developer may dispatch a promotion to the one or more users determined to be promotion eligible.

Abstract: A machine learning technique may be applied to applications hosted by an application store to extract features that can be utilized to train one or more classifiers of the applications based on their relative complexity. A processor may receive pairwise comparisons of relative complexity and feature representations for the applications to be used in training of a classifier. The processor may determine a feature set that is correlated with the pairwise comparison of relative complexity and obtain a classifier based thereupon.

Abstract: A developer/requestor may select a subset of users for a promotion by querying a database; however, the developer may not be provided with specific details about each user (e.g., purchase history, browsing history, etc.). The system returns a response to the query that obfuscates a user's data while simultaneously providing the developer with a response to indicate to the developer whether or not a user or group of users is promotion eligible based on the developer's criteria. The developer may dispatch a promotion to the one or more users determined to be promotion eligible.