Story
Palantir
Genuine PartsPalantir at Genuine Parts: Feature Pipelines
Genuine Parts, a consumer discretionary organization in the United States, uses Ontology from Palantir to support applied machine learning for data scientists and ML engineers.
A Ontology rollout at Genuine Parts took applied machine learning out of inboxes and into a named queue.
“From the United States, consumer discretionary teams still had to serve customers and internal partners who expected a straight answer.”
“Palantir builds data operating systems and AI platforms used by governments and enterprises to integrate operational data and decision workflows.”
Independent write-up. Figures come from public sources or are illustrative.
Value results
| Category | Value result |
|---|---|
| Capability | Data scientists and ML engineers work from the same Ontology record for feature pipelines |
| Capability | Feature pipelines can be reviewed without waiting on a personal export |
| Capability | Named workflow replaces ad hoc routing for feature pipelines |
Story
Genuine Parts grew feature pipelines faster than the local tools around it. From the United States, consumer discretionary 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 Ontology put applied machine learning on Palantir. Palantir builds data operating systems and AI platforms used by governments and enterprises to integrate operational data and decision workflows. Genuine Parts 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.
