Abstract: A network-based computing system can implement a transaction service in which digital wallets of client transaction entities are dynamically monitored to maintain a balance within a liquidity tranche unique to each transaction entity. The computing system can further execute a smart liquidity model that parses transaction requests into smaller clips and execution timing intervals such that risk of transaction failure is minimized or eliminated.

Abstract: A network-based computing system can implement a transaction service in which digital wallets of client transaction entities are dynamically monitored to maintain a balance within a liquidity tranche unique to each transaction entity. The computing system can further execute a smart liquidity model that parses transaction requests into smaller clips and execution timing intervals such that risk of transaction failure is minimized or eliminated.

Abstract: Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.

Abstract: Techniques for automated machine learning (ML) pipeline exploration and deployment are described. An automated ML pipeline generation system allows users to easily construct optimized ML pipelines by providing a dataset, identifying a target column in the dataset, and providing an exploration budget. Multiple candidate ML pipelines can be identified and evaluated through an exploration process, and a best ML pipeline can be provided to the requesting user or deployed for production inference. Users can configure, monitor, and adapt the exploration at multiple points in time throughout.

Abstract: Systems and methods are disclosed for emotive healthcare software usability. A method to improve software usability is described, the method comprising presenting a software application to a user. The method also including logging activities of the user with respect to the software application, wherein the logging includes recording the user using the software application, and wherein the activities include user action with respect to the software application and mouse location on a user interface displaying the software application. The method also including interpreting user emotion from the recording, and tracking an emotive index based on a combination of user emotion and user action with respect to the software application and mouse location. The method also including providing feedback based on the emotive index.

Abstract: Systems and methods are disclosed for emotive healthcare software usability. A method to improve software usability is described, the method comprising presenting a software application to a user. The method also including logging activities of the user with respect to the software application, wherein the logging includes recording the user using the software application, and wherein the activities include user action with respect to the software application and mouse location on a user interface displaying the software application. The method also including interpreting user emotion from the recording, and tracking an emotive index based on a combination of user emotion and user action with respect to the software application and mouse location. The method also including providing feedback based on the emotive index.