Story
Confluent
MollieHow Mollie Runs Applied Machine Learning on Apache Kafka
Mollie, a financials organization in the Netherlands, uses Apache Kafka from Confluent to support applied machine learning for data scientists and ML engineers.
Value results
| Category | Value result |
|---|---|
| Capability | Named workflow replaces ad hoc routing for feature pipelines |
| Capability | Applied machine learning stays visible to adjacent teams through Apache Kafka |
| Capability | Data scientists and ML engineers work from the same Apache Kafka record for feature pipelines |
Story
Mollie is based in the Netherlands and runs financials operations at a scale where feature pipelines cannot live in side channels. Data scientists and ML engineers were reconciling competing copies of the same work, which slowed applied machine learning and hid who owned the next step.
The company runs applied machine learning on Confluent, with Apache Kafka as the product data scientists and ML engineers actually open. Confluent is the data streaming platform built around Apache Kafka, used to move events between applications in real time. For Mollie, that means data scientists and ML engineers can open one workflow, see feature pipelines, and let neighboring teams join without inventing a parallel stack.
Public materials confirm the companies and products. They do not always publish a single verified KPI for this pairing, so the outcome here is operational: clearer ownership, fewer stalled handoffs, and a shared record for feature pipelines.
