This project provides a machine learning meta-model that generates synthetic data for benchmarking and testing ML pipelines. It defines ML artifacts including projects, feature sets, feature vectors, pipelines, and models in a solution-agnostic format. The framework enables comparison of ML platform capabilities and regression testing across versions of MLRun/Iguazio/Nuclio deployments through unit, integration, and acceptance tests.