Abstract: The subject technology receives, in an application on an electronic device, a message, the message being associated with a user and including information in a header portion of the message. The subject technology determines, on the electronic device, a current state of messaging activity of the user based at least in part on a log of previous events associated with the user, where the log of previous events includes information that has been hashed using a cryptographic hash function. The subject technology determines, on the electronic device using a set of rules provided by a machine learning model, that the user is likely to view the message based on the current state of the messaging activity of the user. The subject technology sets, on the electronic device, an indication that the message is important based on the determining.

Abstract: The subject technology receives information associated with a machine learning model. The subject technology determines a set of metrics based at least in part on the information associated with the machine learning model, where the set of metrics corresponds to respective indicators of performance of the machine learning model based on input data from a data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set. Further, the subject technology displays a user interface based at least in part on the set of metrics, where the user interface includes a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model.

Abstract: The subject technology receives, in an application on an electronic device, a message, the message being associated with a user and including information in a header portion of the message. The subject technology determines, on the electronic device, a current state of messaging activity of the user based at least in part on a log of previous events associated with the user, where the log of previous events includes information that has been hashed using a cryptographic hash function. The subject technology determines, on the electronic device using a set of rules provided by a machine learning model, that the user is likely to view the message based on the current state of the messaging activity of the user. The subject technology sets, on the electronic device, an indication that the message is important based on the determining.

Abstract: The subject technology receives information associated with a machine learning model. The subject technology determines a set of metrics based at least in part on the information associated with the machine learning model, where the set of metrics corresponds to respective indicators of performance of the machine learning model based on input data from a data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set. Further, the subject technology displays a user interface based at least in part on the set of metrics, where the user interface includes a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model.