Story
Qlik
Marathon PetroleumHow Marathon Petroleum Runs Applied Machine Learning on Qlik Sense
Marathon Petroleum, an energy organization in the United States, uses Qlik Sense from Qlik to support applied machine learning for data scientists and ML engineers.
In the United States, Marathon Petroleum relies on Qlik for applied machine learning.
“Energy work at Marathon Petroleum spans more than one site, even when headquarters sits in the United States.”
“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 |
|---|---|
| Capability | Data scientists and ML engineers work from the same Qlik Sense 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
Energy work at Marathon Petroleum 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.
Qlik (Qlik Sense) is what they standardized on. Qlik provides analytics and data integration software, including Qlik Sense and Talend, for interactive analysis and pipelines. Marathon Petroleum 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 The story is about operating change, not an unpublished percentage.
