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Snowflake@Casino Guichard

Casino Guichard Brings Feature Pipelines onto Snowflake

Casino Guichard, a consumer staples organization in France, uses Cortex AI 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
Risk and complianceCortex AI is the governed place data scientists and ML engineers use for applied machine learning
CapabilityNew joiners can see how applied machine learning actually runs

Story

Inside Casino Guichard, applied machine learning used to depend on whoever still had the latest file. That pattern is common in consumer staples groups working out of France. Data scientists and ML engineers needed a system that would still make sense after the original project team moved on.

Casino Guichard uses Cortex AI from Snowflake as the working layer for feature pipelines. Snowflake is a cloud data platform that separates storage and compute so organizations can share, analyze, and govern data across clouds. The practical change is simple: applied machine learning has a home, and reviews happen there instead of in a forwarded thread.

Nothing in this writeup invents a savings number. What Casino Guichard gets from Snowflake is a durable place to run applied machine learning and a way for data scientists and ML engineers to see the same feature pipelines at the same time.

Relationship map

Casino Guichard uses Snowflake, Adobe, Confluent, Genesys, Shopify, Databricks, Pega, CyberArk, Twilio, Datadog. Shared with Aviva, FIS, HelloFresh, Jeld-Wen, Matillion. Industry: Consumer Staples. Value: Productivity, Risk and compliance, Capability. Drag nodes, filter types, or expand a node to follow more commonalities.

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