Case Study: S&P Global achieves faster, more efficient machine learning scaling with Snowflake

S&P Global Market Intelligence Saves Time and Money While Scaling Machine Learning

S&P Global Market Intelligence needed a more efficient way to process terabytes of data each week and scale machine learning workflows for risk assessment and credit insights. Using Snowflake, including Snowpark and Snowpark Container Services, the team was able to unify storage and compute, reduce the overhead of data transfers, and run multiple ML models in parallel across 200M+ webpages.

With Snowflake, S&P Global increased efficiency, simplified operations, and cut downtime, manual configuration, and maintenance. The impact included faster processing of millions of webpages in minutes, optimized GPU usage for compute-intensive tasks, and better historical audit tracking to compare default scores over time.

S&P Global

Moody Hadi

Head of Risk Solutions New Product Development