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: Automatically identifying context-specific repeated transformations (such as repeated edit tasks) that are based on observation of the developer drafting or modifying code. As the developer modifies the code, the code passes through a series of states, one after the other. The computing system observes the series of states of the code. It is based on this observation that the computing system identifies repeated transformations of the code for potentially offering to continue performing the repeated transformations for the user. This alleviates the developer from having to manually perform the remainder of the repeated transformations.

Abstract: Automatically identifying context-specific repeated transformations (such as repeated edit tasks) that are based on observation of the developer drafting or modifying code. As the developer modifies the code, the code passes through a series of states, one after the other. The computing system observes the series of states of the code. It is based on this observation that the computing system identifies repeated transformations of the code for potentially offering to continue performing the repeated transformations for the user. This alleviates the developer from having to manually perform the remainder of the repeated transformations.

Abstract: Improved techniques for asynchronously displaying the results of a codebase analysis service are provided herein. Initially, machine learning is applied to a corpus of model code. In doing so, a machine learning model is generated, where the model identifies coding practices that are included in the corpus of model code. After this model is generated, then the model is applied to a current codebase by comparing coding practices of the current codebase to the identified coding practices that were extracted, or rather identified, from the corpus of model code. Then, in response to detecting one or more differences between the current codebase's coding practices and the identified coding practices, where the differences satisfy a pre-determined difference threshold, a user interface is caused to display one or more insights. These insights beneficially provide additional detailed information describing the differences.

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: Improved techniques for asynchronously displaying the results of a codebase analysis service are provided herein. Initially, machine learning is applied to a corpus of model code. In doing so, a machine learning model is generated, where the model identifies coding practices that are included in the corpus of model code. After this model is generated, then the model is applied to a current codebase by comparing coding practices of the current codebase to the identified coding practices that were extracted, or rather identified, from the corpus of model code. Then, in response to detecting one or more differences between the current codebase's coding practices and the identified coding practices, where the differences satisfy a pre-determined difference threshold, a user interface is caused to display one or more insights. These insights beneficially provide additional detailed information describing the differences.

Abstract: The present invention allows a user or community of users to rate content across a variety of web sites and display contextual sensitive reviews. Rather than the rating information being controlled by the web site owner, the rating information may be owned and controlled by a third party. Users have the ability to rate a web site, review ratings from a web site, or operate a web site rating system.