Abstract: In some implementations, a device may receive input data for machine learning model training. The device may generate, based on the input data, artificial data using one or more machine learning models operating on one or more servers, wherein the one or more machine learning models includes a generative artificial intelligence model configured to generate the artificial data such that the artificial data shares a set of common characteristics with the input data. The device may train, using the artificial data and metadata associated with the artificial data, a particular machine learning model. The device may transmit an output associated with the particular machine learning model.

Abstract: Methods and systems for generating, storing, and modifying data for feature engineering purposes. In particular, methods and systems for automatically generating feature engineering pipelines by chaining multiple transformers (models and/or function trained to transform data in specific ways) and estimators (e.g., algorithms trained with input data to generate transformers) in a sequential order based on feature attributes. To determine the specific transformers and estimators, the system may access a shared knowledge database, graph engine, and pipeline engine.

Abstract: Methods and systems for generating, storing, and modifying data for feature engineering purposes. In particular, methods and systems for automatically generating feature engineering pipelines by chaining multiple transformers (models and/or function trained to transform data in specific ways) and estimators (e.g., algorithms trained with input data to generate transformers) in a sequential order based on feature attributes. To determine the specific transformers and estimators, the system may access a shared knowledge database, graph engine, and pipeline engine.