Abstract: This disclosure is directed to the automated generation of Machine Learning (ML) models. The system receives a user directive containing one or more requirements for building the ML model. The system further identifies common requirements between the user directive and one or more prior user directives and associates characteristics of the prior user directive, or model generated therefrom, with the user directive. The system further associates performance values generated by continuous monitoring of deployed ML models to individual characteristics of the user directive used to generate each of the deployed ML models. The system continuously improves model generation efficiency, model performance, and first run performance of individual ML models by learning from the improvements made to one or more prior ML models having similar characteristics.

Abstract: Methods and computer-readable media are provided for defining user intent so that user intent can be determined and advertisements and other information can be provided to a user based on that user's intent. A topical expression is identified and is associated with attributes and actions. Actions indicate steps that can be performed to achieve a task associated with the topical expression. An intent structure is then generated. The intent structure illustrates a relationship between the identified topical expression and other topical expressions. User data may then be received and mapped to the intent structures to determine present and future user intent.