Story
MongoDB
OlamOlam Modernizes Feature Pipelines with MongoDB
Olam, a consumer staples organization in Singapore, uses MongoDB Atlas from MongoDB to support applied machine learning for data scientists and ML engineers.
Value results
| Category | Value result |
|---|---|
| Productivity | Handoffs in applied machine learning sit in a shared queue instead of a mailbox trail |
| Risk and compliance | MongoDB Atlas is the governed place data scientists and ML engineers use for applied machine learning |
| Capability | New joiners can see how applied machine learning actually runs |
Story
Olam is based in Singapore and runs consumer staples 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 MongoDB Atlas 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 Olam, 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.
