Story
MongoDB
HanwhaHanwha Standardizes Feature Pipelines on MongoDB
Hanwha, an industrials organization in South Korea, uses Atlas Search from MongoDB to support applied machine learning for data scientists and ML engineers.
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 | Atlas Search is the governed place data scientists and ML engineers use for applied machine learning |
Story
Hanwha is based in South Korea and runs industrials 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 MongoDB, with Atlas Search as the product data scientists and ML engineers actually open. MongoDB provides a developer data platform, with Atlas as the managed service for document, search, and vector workloads. For Hanwha, 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.
