Are you a Data Scientist who loves using Python and Jupyter Notebook but is having difficulty building performant analytics and machine-learning at scale? Don't despair - Teradata Vantage and Teradata Package for Python (teradataml) are here to help. They enable performant execution of complex analytics on large datasets, while using your favorite data science tools and language.
In this 5th episode of the Using Python with Vantage TechBytes series, Alexander Kolovos demonstrates how to train and score multiple models (micromodeling) in parallel and at scale using Vantage and its SCRIPT Table Operator. It also includes a demonstration for map_row() function for row-based operations and map_partition() function for partition-based operations.
Download Jupyter notebook used in the demonstration from a Teradata GitHub site: https://github.com/Teradata/techbytes-using-python-with-vantage