Story
Databricks
PayPalDatabricks Supports Applied Machine Learning at PayPal
PayPal, a financials organization in the United States, uses Databricks Data Intelligence Platform from Databricks to support applied machine learning for data scientists and ML engineers.
In the United States, PayPal relies on Databricks for applied machine learning.
“From the United States, financials teams still had to serve customers and internal partners who expected a straight answer.”
“Rolling out Databricks Data Intelligence Platform put applied machine learning on Databricks.”
Independent write-up. Figures come from public sources or are illustrative.
Value results
| Category | Value result |
|---|---|
| Productivity | Fewer stalled items because feature pipelines has a clear owner |
| Productivity | Handoffs in applied machine learning sit in a shared queue instead of a mailbox trail |
| Risk and compliance | Databricks Data Intelligence Platform is the governed place data scientists and ML engineers use for applied machine learning |
Story
PayPal grew feature pipelines faster than the local tools around it. From the United States, financials teams still had to serve customers and internal partners who expected a straight answer. Data scientists and ML engineers were the ones stitching the picture together by hand.
Rolling out Databricks Data Intelligence Platform put applied machine learning on Databricks. Databricks provides a lakehouse platform for data engineering, analytics, and AI, with Unity Catalog and Mosaic AI for governed models. PayPal keeps the product in the path where work already happens, so data scientists and ML engineers do not context-switch into a graveyard system used only for audits.
The visible result is steadier applied machine learning. Feature pipelines is easier to inspect, and adjacent groups can join data scientists and ML engineers without a guided tour of someone's desktop.
