Case Study: FiscalNote achieves 3x faster ML model deployment with Databricks

Scaling global policy and intelligence for a better tomorrow

FiscalNote, a software company that provides policy and geopolitical intelligence from legislative and regulatory data, was struggling to deploy AI/ML models across its mix of structured and unstructured data. Before using Databricks, the company had to piece together multiple components for each deployment, which slowed workflows, limited updates to about once a year, and made it difficult to deliver timely, no-disruption model changes.

By adopting Databricks and its unified data analytics platform with AI/ML tools, FiscalNote streamlined model deployment and reduced the need for custom stitching and coding. The result was a deployment process that was 3x faster, along with higher analyst productivity and a better ability to keep models current for customers.

FiscalNote

Vlad Eidelman

Chief Technology Officer