Scalable Feature Engineering Using Python

Do you know that companies that strategically scale AI are achieving 3x return?
Python libraries such as Pandas and Scikit-Learn come with preprocessing functions that accelerate the development of machine learning (ML) features. Yet, these functions are not designed for scalable and parallel processing in production.

Join us on 16 December to learn more about:

• The challenges preventing a repeatable and scalable path to production
• How to accelerate the development of ML features
• How to leverage Snowpark functions as building blocks to recreate popular preprocessing functions in Pandas and Scikit-Learn for scalable ML pipelines.