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Reliability layer for delayed-label ML under distribution shift
1 starsPython
A controller to automatically mitigate model drift
by pberlizov·Jun 9, 2026·1 point·1 comment
AI Analysis
●●SolidNiche GemBig Brain
Handles 12-week label delays in fraud models without scheduled retrains.
Strengths
- •Distinguishes shifts needing intervention from those that self-resolve.
- •Learns from delayed feedback like chargebacks and disputes.
- •Benchmarked across 3 public fraud streams with measurable risk reduction.
Weaknesses
- •Narrow applicability to delayed-label scenarios only.
- •Requires PyTorch integration for deployment.
Category
Target Audience
ML engineers in fraud, AML, delayed-label scenarios
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