Story
MongoDB
JetBlueJetBlue Uses Atlas Vector Search for Feature Pipelines
JetBlue, an industrials organization in the United States, uses Atlas Vector Search from MongoDB 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 Atlas Vector Search |
| Capability | Data scientists and ML engineers work from the same Atlas Vector Search record for feature pipelines |
Story
Industrials work at JetBlue spans more than one site, even when headquarters sits in the United States. 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.
MongoDB (Atlas Vector Search) is what they standardized on. MongoDB provides a developer data platform, with Atlas as the managed service for document, search, and vector workloads. JetBlue 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 The story is about operating change, not an unpublished percentage.
