From 2012 to 2018, blue-chip technology companies implemented custom-built ML platforms for internal use (i.e., Facebook’s FBLearning, Uber’s Michelangelo, Twitter’s Cortex, AirBnB’s BigHead), many of these platforms are primarily based on open-source packages and have been deeply tailored for the specific use cases of their respective companies. Since then, the industry has seen a strong evolution of Enterprise-grade ML platform solutions, including those from incumbent vendors (e.g., Amazon Sagemaker, Microsoft Azure ML, Google Cloud ML, etc.) and the challengers in the space (e.g., DataRobot, H2O, BigML, Dataiku). Incumbent vendors follow an incremental strategy approach, with their ML services offering sitting on top of their existing cloud services as another application layer vs. the MLnativeapproach taken by the challengers. As adoption of ML increases, many Enterprises are quickly turning tooff-the-shelfData Science & Machine Learning Platforms to accelerate time to market, reduce costs of operationalization, and increase success ratio (number of ML models deployed and operationalized).