Part 3 — Why Most Machine Learning Models Fail After Deployment
中文摘要
本文探讨了机器学习模型在部署后即便初期指标优异也常失效的原因，分析了数据偏移及现实环境带来的挑战。
English Summary
This article explores why machine learning models often fail after deployment despite strong initial metrics, focusing on real-world challenges like data drift and environmental changes.
Original Excerpt
There’s a moment every AI and Data Science Engineer eventually runs into. The model works — the metrics look good, the validation results… Continue reading on AI Engineering in the Real World »