Abstract: Systems and methods are provided for implementing soft model assertions (SMA) system and techniques designed to monitor and improve Machine Learning (ML) model quality by to detecting errors within the one or more ML models. SMA techniques and systems are distinctly designed to leverage: 1) a user's ability to specify features over data; and 2) large, existing datasets of organizations, in a manner that can improve the accuracy and quality of predicting potential errors in Machine Learning (ML) models. A SMA system can include a controller device receiving predictions generated based on the ML models and output from the SMA system. The controller performs autonomous operations of the system in response to determining that the one or more detected errors within the one or more ML models yield a high certainty of errors in the predictions. The SMA system also includes a domain specific language and a severity score module.

Abstract: Systems and methods are provided for implementing soft model assertions (SMA) system and techniques designed to monitor and improve Machine Learning (ML) model quality by to detecting errors within the one or more ML models. SMA techniques and systems are distinctly designed to leverage: 1) a user's ability to specify features over data; and 2) large, existing datasets of organizations, in a manner that can improve the accuracy and quality of predicting potential errors in Machine Learning (ML) models. A SMA system can include a controller device receiving predictions generated based on the ML models and output from the SMA system. The controller performs autonomous operations of the system in response to determining that the one or more detected errors within the one or more ML models yield a high certainty of errors in the predictions. The SMA system also includes a domain specific language and a severity score module.

Abstract: Systems and methods are provided for implementing soft model assertions (SMA) system and techniques designed to monitor and improve Machine Learning (ML) model quality by to detecting errors within the one or more ML models. SMA techniques and systems are distinctly designed to leverage: 1) a user's ability to specify features over data; and 2) large, existing datasets of organizations, in a manner that can improve the accuracy and quality of predicting potential errors in Machine Learning (ML) models. A SMA system can include a controller device receiving predictions generated based on the ML models and output from the SMA system. The controller performs autonomous operations of the system in response to determining that the one or more detected errors within the one or more ML models yield a high certainty of errors in the predictions. The SMA system also includes a domain specific language and a severity score module.

Abstract: Disclosed herein are computer devices, systems, and methods for enabling persistent data connections with a vehicle and low-latency vehicle commands. A driver can use an app installed on a mobile device to send commands to a vehicle's DCM, which can execute such commands on one or more vehicle systems, such as remote ignition and door lock systems. Upon app initialization, a wakeup command can be sent to the DCM through one or more intermediary servers to establish a persistent data connection between a data center server and the DCM. Then, when the driver issues a command from the app, the command can be communicated between the data center server and the DCM directly over the persistent data connection, with lower latency. After a predetermined timeout period in which the driver has not used the mobile app, the persistent data connection can be terminated.

Abstract: Disclosed herein are computer devices, systems, and methods for enabling persistent data connections with a vehicle and low-latency vehicle commands. A driver can use an app installed on a mobile device to send commands to a vehicle's DCM, which can execute such commands on one or more vehicle systems, such as remote ignition and door lock systems. Upon app initialization, a wakeup command can be sent to the DCM through one or more intermediary servers to establish a persistent data connection between a data center server and the DCM. Then, when the driver issues a command from the app, the command can be communicated between the data center server and the DCM directly over the persistent data connection, with lower latency. After a predetermined timeout period in which the driver has not used the mobile app, the persistent data connection can be terminated.