Story
Dataiku
RWERWE Brings Feature Pipelines onto Dataiku
RWE, a utilities organization in Germany, uses MLOps from Dataiku to support applied machine learning for data scientists and ML engineers.
RWE standardized feature pipelines on Dataiku instead of keeping parallel copies.
“Data scientists and ML engineers asked for a shared way to run applied machine learning without freezing local judgment.”
“Dataiku is a universal AI platform that lets data teams and analysts collaborate on pipelines, models, and governed AI applications.”
Independent write-up. Figures come from public sources or are illustrative.
Value results
| Category | Value result |
|---|---|
| Productivity | Fewer stalled items because feature pipelines has a clear owner |
| Productivity | Handoffs in applied machine learning sit in a shared queue instead of a mailbox trail |
| Risk and compliance | MLOps is the governed place data scientists and ML engineers use for applied machine learning |
Story
Utilities work at RWE spans more than one site, even when headquarters sits in Germany. 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.
Dataiku (MLOps) is what they standardized on. Dataiku is a universal AI platform that lets data teams and analysts collaborate on pipelines, models, and governed AI applications. RWE 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 The story is about operating change, not an unpublished percentage.
