Story
MongoDB
HermesHermes Modernizes Feature Pipelines with MongoDB
Hermes, a consumer discretionary organization in France, uses MongoDB Atlas from MongoDB to support applied machine learning for data scientists and ML engineers.
Hermes runs applied machine learning on MongoDB so ownership is obvious when work stalls.
“From France, consumer discretionary teams still had to serve customers and internal partners who expected a straight answer.”
“MongoDB provides a developer data platform, with Atlas as the managed service for document, search, and vector workloads.”
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 |
| Capability | New joiners can see how applied machine learning actually runs |
Story
Hermes grew feature pipelines faster than the local tools around it. From France, consumer discretionary teams still had to serve customers and internal partners who expected a straight answer. Data scientists and ML engineers were the ones stitching the picture together by hand.
Rolling out MongoDB Atlas put applied machine learning on MongoDB. MongoDB provides a developer data platform, with Atlas as the managed service for document, search, and vector workloads. Hermes keeps the product in the path where work already happens, so data scientists and ML engineers do not context-switch into a graveyard system used only for audits.
The visible result is steadier applied machine learning. Feature pipelines is easier to inspect, and adjacent groups can join data scientists and ML engineers without a guided tour of someone's desktop.
