Abstract: Inferences may be obtained to handle access requests at a non-relational database system. An access request may be received at a non-relational database system. The non-relational database system may determine that the access request uses a machine learning model to complete the access request. The non-relational database system may cause an inference to be generated using data items for the access request as input to the machine learning model. The access request may be completed using the generated inference.

Abstract: In implementations of systems for stylizing digital content, a computing device receives digital content having a plurality of content entities. Classified content entities are generated by classifying the plurality of content entities using one or more machine-learning models. A determination is then made regarding correspondence of the classified content entities with visual styles of a digital template. Based on the determined correspondence, the plurality of content entities of the digital content are displayed as having the visual styles, respectively, of the digital template.

Abstract: In implementations of systems for stylizing digital content, a computing device implements a style system to receive input data describing digital content to be stylized based on visual styles of example content included in a digital template. The style system generates embeddings for content entities included in the digital content using a machine learning model. Classified content entities are determined based on the embeddings using the machine learning model. The style system generates an output digital template that includes portions of the digital content having the visual styles of example content included in the digital template based on the classified content entities.

Abstract: In implementations of systems for stylizing digital content, a computing device implements a style system to receive input data describing digital content to be stylized based on visual styles of example content included in a digital template. The style system generates embeddings for content entities included in the digital content using a machine learning model. Classified content entities are determined based on the embeddings using the machine learning model. The style system generates an output digital template that includes portions of the digital content having the visual styles of example content included in the digital template based on the classified content entities.

Abstract: Inferences may be obtained to handle access requests at a non-relational database system. An access request may be received at a non-relational database system. The non-relational database system may determine that the access request uses a machine learning model to complete the access request. The non-relational database system may cause an inference to be generated using data items for the access request as input to the machine learning model. The access request may be completed using the generated inference.

Abstract: Non-relational database operations are invoked to provide results consistent with commands specified in a relational query language. A request to access data in a non-relational database is received that includes one or more commands specified in a query language for accessing data according to a relational data scheme. The request may be evaluated to determine one or more Application Programming Interfaces (APIs) to invoke operations at the non-relational database that provide a result consistent with the commands specified in the query language. The determined APIs are executed and a result returned for the request based on the execution of the APIs.

Abstract: Inferences may be obtained to handle access requests at a non-relational database system. An access request may be received at a non-relational database system. The non-relational database system may determine that the access request uses a machine learning model to complete the access request. The non-relational database system may cause an inference to be generated using data items for the access request as input to the machine learning model. The access request may be completed using the generated inference.