Story
Teradata
AtkinsRealisAtkinsRealis Standardizes Feature Pipelines on Teradata
AtkinsRealis, an industrials organization in Canada, uses ClearScape Analytics from Teradata to support applied machine learning for data scientists and ML engineers.
Teams at AtkinsRealis moved feature pipelines onto Teradata after local tools started to collide.
“Industrials work at AtkinsRealis spans more than one site, even when headquarters sits in Canada.”
“Data scientists and ML engineers asked for a shared way to run applied machine learning without freezing local judgment.”
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 |
| Risk and compliance | ClearScape Analytics 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
Industrials work at AtkinsRealis spans more than one site, even when headquarters sits in Canada. 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.
Teradata (ClearScape Analytics) is what they standardized on. Teradata provides a connected multi-cloud data platform for large-scale analytics and mixed workload warehousing. AtkinsRealis 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 The story is about operating change, not an unpublished percentage.
