Abstract: Providing custom machine learning models to client computer systems. Multiple machine learning models are accessed. Client-specific data for multiple client computer systems are also accessed. For each of at least some of the client computer systems, performing the following actions: First, using the corresponding client-specific data for the corresponding client computer system to determine which subset of the multiple machine learning models is applicable to the corresponding client computer system. The subset of the multiple machine learning models includes more than one of the multiple machine learning models. Then, aggregating the determined subset of the multiple machine learning models to generate an aggregated subset of machine learning models that is customized to the corresponding client computer system.

Abstract: Access control enhancements reduce security risks and management burdens when a user with access to a primary asset seeks access to a related supplementary asset. When a sufficient proof of access to the primary asset is provided, and the relationship of the primary and supplementary assets is recognized, access to the supplementary asset is granted without requiring a separate sign-in, a permission query to the supplementary asset's owner, or an authorization through an authenticated identity of the requestor, for example. Automatic access to the supplementary asset can be granted without the security risks inherent in a file share or a share link. In particular, a developer with access to one component of a project can be automatically and conveniently granted access to the rest of the project. Likewise, a custom machine learning model for autocompletion becomes accessible to all developers working on the repository source used to train the model.

Abstract: Providing custom machine learning models to client computer systems. Multiple machine learning models are accessed. Client-specific data for multiple client computer systems are also accessed. For each of at least some of the client computer systems, performing the following actions: First, using the corresponding client-specific data for the corresponding client computer system to determine which subset of the multiple machine learning models is applicable to the corresponding client computer system. The subset of the multiple machine learning models includes more than one of the multiple machine learning models. Then, aggregating the determined subset of the multiple machine learning models to generate an aggregated subset of machine learning models that is customized to the corresponding client computer system.

Abstract: Providing custom machine learning models to client computer systems. Multiple machine learning models are accessed. Client-specific data for multiple client computer systems are also accessed. For each of at least some of the client computer systems, performing the following actions: First, using the corresponding client-specific data for the corresponding client computer system to determine which subset of the multiple machine learning models is applicable to the corresponding client computer system. The subset of the multiple machine learning models includes more than one of the multiple machine learning models. Then, aggregating the determined subset of the multiple machine learning models to generate an aggregated subset of machine learning models that is customized to the corresponding client computer system.

Abstract: Access control enhancements reduce security risks and management burdens when a user with access to a primary asset seeks access to a related supplementary asset. When a sufficient proof of access to the primary asset is provided, and the relationship of the primary and supplementary assets is recognized, access to the supplementary asset is granted without requiring a separate sign-in, a permission query to the supplementary asset's owner, or an authorization through an authenticated identity of the requestor, for example. Automatic access to the supplementary asset can be granted without the security risks inherent in a file share or a share link. In particular, a developer with access to one component of a project can be automatically and conveniently granted access to the rest of the project. Likewise, a custom machine learning model for autocompletion becomes accessible to all developers working on the repository source used to train the model.

Abstract: The behavior of a machine learning model and the training dataset used to train the model are monitored to determine when the accuracy of the model's predictions indicate that the model should be retrained. The retraining is determined from one or more precision metrics and a coverage metric that are generated during operation of the model. A precision metric measures the ability of the model to make predictions that are accepted by an inference system and the coverage metric measures the ability of the model to make predictions given a set of input features. In addition, changes made to the training dataset are analyzed and used as an indication of when the model should be retrained.

Abstract: Improving the results and process of machine learning service in computer program development. A client's codebase is accessed. A set of features are extracted from the client's codebase. One or more features from the set of features are then selected. Thereafter, at least one of the selected features is sent to a machine learning service that uses the received feature(s) to build custom model(s) for the client's computer system.

Abstract: Providing custom machine learning models to client computer systems. Multiple machine learning models are accessed. Client-specific data for multiple client computer systems are also accessed. For each of at least some of the client computer systems, performing the following actions: First, using the corresponding client-specific data for the corresponding client computer system to determine which subset of the multiple machine learning models is applicable to the corresponding client computer system. The subset of the multiple machine learning models includes more than one of the multiple machine learning models. Then, aggregating the determined subset of the multiple machine learning models to generate an aggregated subset of machine learning models that is customized to the corresponding client computer system.

Abstract: Improving the results and process of machine learning service in computer program development. A client's codebase is accessed. A set of features are extracted from the client's codebase. One or more features from the set of features are then selected. Thereafter, at least one of the selected features is sent to a machine learning service that uses the received feature(s) to build custom model(s) for the client's computer system.

Abstract: Systems and methods for sending in-product notifications to individual users of a software product or a specifically identified subset of users of the software product selected via their previously observed interactions with the software product. In addition, targeted notifications of bug fixes can be sent to specific users who have encountered an error condition or performance issue that a particular bug fix is designed to correct.

Abstract: Systems and methods for sending in-product notifications to individual users of a software product or a specifically identified subset of users of the software product selected via their previously observed interactions with the software product. In addition, targeted notifications of bug fixes can be sent to specific users who have encountered an error condition or performance issue that a particular bug fix is designed to correct.