Story
Palantir
Sherwin-WilliamsSherwin-Williams Standardizes Feature Pipelines on Palantir
Sherwin-Williams, a materials organization in the United States, uses Palantir AIP from Palantir to support applied machine learning for data scientists and ML engineers.
Value results
| Category | Value result |
|---|---|
| Capability | Feature pipelines can be reviewed without waiting on a personal export |
| Capability | Named workflow replaces ad hoc routing for feature pipelines |
| Capability | Applied machine learning stays visible to adjacent teams through Palantir AIP |
Story
Materials work at Sherwin-Williams spans more than one site, even when headquarters sits in the United States. 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.
Palantir (Palantir AIP) is what they standardized on. Palantir builds data operating systems and AI platforms used by governments and enterprises to integrate operational data and decision workflows. Sherwin-Williams 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 The story is about operating change, not an unpublished percentage.
