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AI & ML·11 min read·
Feature stores for real-time credit decisions
Batch features died when payroll data went live. How we serve consistent vectors across training and inference.
Rise Engineering
Platform & infrastructure
Training-serving skew is the silent killer of credit models. A feature store isn't vanity — it's insurance against midnight incidents.
We pin point-in-time correctness for income and balance signals. Every feature documents freshness SLA and fallback behavior.
Offline backfills use the same transformation code paths as online serving — enforced by CI, not policy.
Start with ten well-owned features before chasing thousands of columns.
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