Abstract: A computing platform may be configured to (i) obtain an input dataset, (ii) construct a graph from the input dataset, (iii) for a given node within the constructed graph, generate a first type of embedding vector using a first embedding technique (e.g., a shallow embedding technique) and a second type of embedding vector using a second embedding technique that differs from the first embedding technique (e.g., a deep embedding technique), and (iv) use the first and second types of embedding vectors for the given node and a data science model to render a given prediction for the given node.

Abstract: A computing platform may be configured to (i) obtain an input dataset, (ii) construct a graph from the input dataset, (iii) for a given node within the constructed graph, generate a first type of embedding vector using a first embedding technique (e.g., a shallow embedding technique) and a second type of embedding vector using a second embedding technique that differs from the first embedding technique (e.g., a deep embedding technique), and (iv) use the first and second types of embedding vectors for the given node and a data science model to render a given prediction for the given node.

Abstract: An example computing platform is configured to receive configuration data that defines a pipeline for building a deep learning model, the configuration data including data defining an input dataset, data type assignments for a set of input data variables included within the dataset, data transformations that are to be applied to the dataset, and a machine learning process that is to be utilized to train the deep learning model. Based on the received configuration data, the computing platform functions to build the deep learning model by obtaining the input dataset, assigning a data type to data in the dataset, selecting transformation operations for the data in the dataset, splitting the dataset into a sequence of data blocks, applying the transformation operations to each data block to produce a transformed dataset, generating a compressed data structure that includes the transformed datasets, and applying the machine learning process to the transformed datasets.