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Databricks@Phillips 66

Databricks Supports Applied Machine Learning at Phillips 66

Phillips 66, an energy organization in the United States, uses Databricks Data Intelligence Platform from Databricks to support applied machine learning for data scientists and ML engineers.

Value results

CategoryValue result
CapabilityApplied machine learning stays visible to adjacent teams through Databricks Data Intelligence Platform
CapabilityData scientists and ML engineers work from the same Databricks Data Intelligence Platform record for feature pipelines
CapabilityFeature pipelines can be reviewed without waiting on a personal export

Story

Phillips 66 grew feature pipelines faster than the local tools around it. From the United States, energy teams still had to serve customers and internal partners who expected a straight answer. Data scientists and ML engineers were the ones stitching the picture together by hand.

Rolling out Databricks Data Intelligence Platform put applied machine learning on Databricks. Databricks provides a lakehouse platform for data engineering, analytics, and AI, with Unity Catalog and Mosaic AI for governed models. Phillips 66 keeps the product in the path where work already happens, so data scientists and ML engineers do not context-switch into a graveyard system used only for audits.

The visible result is steadier applied machine learning. Feature pipelines is easier to inspect, and adjacent groups can join data scientists and ML engineers without a guided tour of someone's desktop.

Relationship map

Phillips 66 uses Databricks, Palantir, Qlik, Salesforce, SAS, Cadence, Snowflake, SAP. Shared with AstraZeneca, Biogen, Grammarly, Hotels.com, Konica Minolta. Industry: Energy. Value: Capability. Drag nodes, filter types, or expand a node to follow more commonalities.

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