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Snowflake@Hershey

Hershey Runs Applied Machine Learning with Snowflake

Hershey, a consumer staples organization in the United States, uses Horizon Catalog from Snowflake to support applied machine learning for data scientists and ML engineers.

Value results

CategoryValue result
ProductivityFewer stalled items because feature pipelines has a clear owner
ProductivityHandoffs in applied machine learning sit in a shared queue instead of a mailbox trail
Risk and complianceHorizon Catalog is the governed place data scientists and ML engineers use for applied machine learning

Story

Hershey did not need another dashboard that nobody opened. It needed applied machine learning to move. In the United States, data scientists and ML engineers already knew where feature pipelines went wrong: too many copies, too little ownership, and a close process that waited on the loudest inbox.

Horizon Catalog from Snowflake is now in that path. Snowflake is a cloud data platform that separates storage and compute so organizations can share, analyze, and govern data across clouds. The company treats it as production tooling for applied machine learning, which is why data scientists and ML engineers live in it rather than exporting from it once a quarter.

Hershey can show how feature pipelines is handled today. That is the value: a repeatable way to run applied machine learning on software the rest of the industry already recognizes.

Relationship map

Hershey uses Snowflake, Microsoft Azure, Nutanix, Netskope, Anthropic, Cloudflare. Shared with Aviva, FIS, HelloFresh, Jeld-Wen, Matillion. Industry: Consumer Staples. Value: Productivity, Risk and compliance. Drag nodes, filter types, or expand a node to follow more commonalities.

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