Story
MongoDB
ApolloHow Apollo Runs Applied Machine Learning on Atlas Search
Apollo, an information technology organization in the United States, uses Atlas Search 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 | Atlas Search 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
Information Technology work at Apollo 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 Search) is what they standardized on. MongoDB provides a developer data platform, with Atlas as the managed service for document, search, and vector workloads. Apollo 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 The story is about operating change, not an unpublished percentage.
