When the System Works but the Data Lies: Notes on Survivorship Bias in Large-Scale ML Pipelines | HackerNoon

Most ML pipelines fail quietly, not through outages, but through data that looks valid while slowly drifting away from reality. Survivorship bias builds when upstream filters distort what the model believes is “truth.” The real work is learning to distrust green dashboards and design pipelines that stay sceptical of their own assumptions.