Story
Databricks
Credit SuisseHow Credit Suisse Runs Applied Machine Learning on Unity Catalog
Credit Suisse, a financials organization in Switzerland, uses Unity Catalog from Databricks to support applied machine learning for data scientists and ML engineers.
Credit Suisse asked for a cleaner operating picture of feature pipelines. Databricks is what they run.
“Financials work at Credit Suisse spans more than one site, even when headquarters sits in Switzerland.”
“Data scientists and ML engineers asked for a shared way to run applied machine learning without freezing local judgment.”
Independent write-up. Figures come from public sources or are illustrative.
Value results
| Category | Value result |
|---|---|
| Productivity | Handoffs in applied machine learning sit in a shared queue instead of a mailbox trail |
| Risk and compliance | Unity Catalog is the governed place data scientists and ML engineers use for applied machine learning |
| Capability | New joiners can see how applied machine learning actually runs |
Story
Financials work at Credit Suisse spans more than one site, even when headquarters sits in Switzerland. Feature pipelines was splitting across regional habits. Data scientists and ML engineers asked for a shared way to run applied machine learning without freezing local judgment.
Databricks (Unity Catalog) is what they standardized on. Databricks provides a lakehouse platform for data engineering, analytics, and AI, with Unity Catalog and Mosaic AI for governed models. Credit Suisse uses it as the system of record for feature pipelines, with data scientists and ML engineers as the primary operators and other groups coming in through the same queue.
Leaders get a picture they can actually walk. Teams get fewer mystery statuses. The story is about operating change, not an unpublished percentage.
