Story
Palantir
AdyenAdyen Extends Apollo Across Applied Machine Learning
Adyen, a financials organization in the Netherlands, uses Apollo from Palantir to support applied machine learning for data scientists and ML engineers.
Apollo became the place Adyen uses to train and ship models on the same lakehouse the business already trusts.
“Inside Adyen, applied machine learning used to depend on whoever still had the latest file.”
“Data scientists and ML engineers needed a system that would still make sense after the original project team moved on.”
Independent write-up. Figures come from public sources or are illustrative.
Value results
| Category | Value result |
|---|---|
| Capability | Applied machine learning stays visible to adjacent teams through Apollo |
| Capability | Data scientists and ML engineers work from the same Apollo record for feature pipelines |
| Capability | Feature pipelines can be reviewed without waiting on a personal export |
Story
Inside Adyen, applied machine learning used to depend on whoever still had the latest file. That pattern is common in financials groups working out of the Netherlands. Data scientists and ML engineers needed a system that would still make sense after the original project team moved on.
Adyen uses Apollo from Palantir as the working layer for feature pipelines. Palantir builds data operating systems and AI platforms used by governments and enterprises to integrate operational data and decision workflows. The practical change is simple: applied machine learning has a home, and reviews happen there instead of in a forwarded thread.
Nothing in this writeup invents a savings number. What Adyen gets from Palantir is a durable place to run applied machine learning and a way for data scientists and ML engineers to see the same feature pipelines at the same time.
