Abstract: Technologies for machine learning-based product classification and approval are described. In some embodiments, a product data record is received and analyzed by machine learning models. The data record includes at least a product description, a product category, and a source organization. A first trained machine learning model is applied to the product description. The first trained machine learning model generates a product type. A second machine learning model is applied to the data record. The second machine learning model produces a classification that includes a confidence score. A decision rule is applied to the classification and the product type. An approval status is generated for the data record.

Abstract: In some embodiments, the disclosed subject matter involves online, or Web-based, automated assistance with documents using examples, and, more specifically, to providing an online user with automatic and quality ranked examples of content that are related to the context of a document that the user is drafting. The quality criteria may be used to train a model to assist in ranking candidate examples. An embodiment uses a resume assistant add-in to a document editor to provide relevant work experience examples to the user to be rendered on a display in proximity to a resume being edited. The add-in communicates with a backend server via an API, where the backend serve pre-processes available content and stores candidate examples having user selectable criteria in key-value form. Other embodiments are described and claimed.