finance_ops · workflow
Stripe builds Shepherd ML feature engineering platform on Chronon to power fraud detection at scale
Stripe's ML feature engineering infrastructure needed to meet strict requirements for both low latency and high feature freshness across massive transaction volumes, and their existing platform could not adequately scale to these demands.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Feature definition via API
ML engineers define features using Chronon's Python- and SQL-based API.
Tools used
ChrononShepherdFlinkAirflowSpark SQLIcebergHive
Outcome
Shepherd's SEPA fraud detection model, with over 200 features, outperforms the previous model and blocks tens of millions of dollars of additional fraud per year, while streaming feature updates achieve p99 freshness of 150ms.
Results
Time saved150ms
Volumeover 200
Cost replacedtens of millions of dollars
Running since2022
Grounding & classification
Source type: technical build writeup
25 fields verified against source quotes, 2 dropped as unverifiable.
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