English(EN) I Trained the Same Model Twice. Why Did I Get Different Results?

MLOps挑战：模型训练的可复现性和随机性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 03:38

一位机器学习工程师详细介绍了他两次训练同一个模型并观察到不同结果的经历。工程师指出，即使代码和超参数相同，随机种子和数据洗牌的差异也会导致结果不同。这凸显了严格的MLOps实践对于确保可复现性和管理模型训练中固有的随机性的重要性。 AI

影响强调了需要强大的MLOps来管理模型训练的变异性并确保可复现性。

排序理由该条目是对MLOps挑战的个人反思，而非主要公告或研究发现。

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Medium — MLOps tag TIER_1 English(EN) · Kaustubh Rewankar · 2026-10-16 03:38

我训练了同一个模型两次。为什么结果不同？

<div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/@rewankarkaustubh/i-trained-the-same-model-twice-why-did-i-get-different-results-52bae7b41b7d?source=rss------mlops-5"><img src="https://cdn-images-1.medium.com/max/2600/1*ybDFqMaMLuiY75GT3xX7u…