Story
Snowflake
Balfour BeattyBalfour Beatty Standardizes Feature Pipelines on Snowflake
Balfour Beatty, an industrials organization in the United Kingdom, uses Snowpark from Snowflake to support applied machine learning for data scientists and ML engineers.
A Snowpark rollout at Balfour Beatty took applied machine learning out of inboxes and into a named queue.
“Industrials work at Balfour Beatty spans more than one site, even when headquarters sits in the United Kingdom.”
“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 |
|---|---|
| Capability | Feature pipelines can be reviewed without waiting on a personal export |
| Capability | Named workflow replaces ad hoc routing for feature pipelines |
| Capability | Applied machine learning stays visible to adjacent teams through Snowpark |
Story
Industrials work at Balfour Beatty spans more than one site, even when headquarters sits in the United Kingdom. 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.
Snowflake (Snowpark) is what they standardized on. Snowflake is a cloud data platform that separates storage and compute so organizations can share, analyze, and govern data across clouds. Balfour Beatty 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 The story is about operating change, not an unpublished percentage.
