I Trained Six Models for Fraud Detection, and the Best One Isn't in Production
Practical example of real-world ML pipeline challenges and dataset integration for builders creating production systems.
AI Summary
A data scientist explains how they built a fraud detection system using multiple models and merged disparate datasets with schema validation, despite a simpler model performing better in tests.
Excerpt
What a final-year project taught me about the gap between evaluation metrics and production decisions The post I Trained Six Models for Fraud Detection, and the Best One Isn't in Production appeared first on Towards Data Science.
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