Story
Palantir
AscensionAscension Extends Apollo Across Applied Machine Learning
Ascension, a health care organization in the United States, uses Apollo from Palantir 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 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 Ascension, applied machine learning used to depend on whoever still had the latest file. That pattern is common in health care groups working out of the United States. Data scientists and ML engineers needed a system that would still make sense after the original project team moved on.
Ascension 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 Ascension 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.
