Story
Databricks@
International Paper
International Paper Standardizes Feature Pipelines on Databricks
International Paper, a materials organization in the United States, uses Unity Catalog from Databricks to support applied machine learning for data scientists and ML engineers.
Value results
| Category | Value result |
|---|---|
| Capability | Applied machine learning stays visible to adjacent teams through Unity Catalog |
| Capability | Data scientists and ML engineers work from the same Unity Catalog record for feature pipelines |
| Capability | Feature pipelines can be reviewed without waiting on a personal export |
Story
International Paper is based in the United States and runs materials operations at a scale where feature pipelines cannot live in side channels. Data scientists and ML engineers were reconciling competing copies of the same work, which slowed applied machine learning and hid who owned the next step.
The company runs applied machine learning on Databricks, with Unity Catalog as the product data scientists and ML engineers actually open. Databricks provides a lakehouse platform for data engineering, analytics, and AI, with Unity Catalog and Mosaic AI for governed models. For International Paper, that means data scientists and ML engineers can open one workflow, see feature pipelines, and let neighboring teams join without inventing a parallel stack.
Public materials confirm the companies and products. They do not always publish a single verified KPI for this pairing, so the outcome here is operational: clearer ownership, fewer stalled handoffs, and a shared record for feature pipelines.
