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世界在模型之下发生变化

初步了解数据漂移和概念漂移。

世界在模型之下发生变化 是 CoddyKit 上的免费 MLOps Academy 课时。这是第 3 节课，共 4 节。你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。这是 MLOps Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 MLOps Academy 课程共包含 4 节课。

本课时的部分内容尚未翻译，以英文显示。

Models Get Stale

Your model is a snapshot of the past. As the real world moves on, that snapshot slowly goes out of date. This slow rot is called drift. 🌍

Data Drift

Data drift means the inputs change shape, like users suddenly being younger or shopping at new hours, even if the right answer rules stay the same.

Concept Drift

Concept drift is deeper: the relationship between inputs and the target changes. The same input now deserves a different prediction than before.

A Real Example

A spam filter faces concept drift constantly. Spammers change tactics, so yesterday's clear spam signal becomes today's normal email. The concept moved.

Sudden vs Gradual

Drift can be sudden, like a lockdown changing behavior overnight, or gradual, creeping over months. Both erode accuracy, just at different speeds.

Seasonal Patterns

Not every shift is permanent. Seasonal changes, like holiday shopping spikes, repeat each year and should not be mistaken for true drift.

Spotting Data Drift

To detect data drift, compare a feature's recent distribution against the training distribution and measure the gap between them.

ref = train['age']
cur = live['age']
# compare distributions, not single values

Spotting Concept Drift

Concept drift is harder because you need real labels. Watch your accuracy over time, and a steady decline hints the concept has shifted.

Drift Is Not a Bug

Drift is not broken code, it is the world doing its job. The only real cure is keeping your model fresh through retraining.

Monitor, Then React

You cannot fix drift you never see. Set up drift monitoring first, then decide whether to alert, retrain, or roll back.

Two Names, One Goal

Remember the split: data drift is inputs changing, concept drift is the answer rule changing. Both signal it may be time to refresh the model.

Quick Check

Tell data drift and concept drift apart.

Recap

The world shifts under every model. Data drift changes inputs, concept drift changes the answer rule. Monitor both and retrain to stay accurate. ✅

常见问题解答

「世界在模型之下发生变化」课时是免费的吗？

是的 — 「世界在模型之下发生变化」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 MLOps Academy 课程的其余内容，请升级到 CoddyKit PRO。 MLOps Academy 课程共包含 4 节课。

「世界在模型之下发生变化」这节课中我会学到什么？

初步了解数据漂移和概念漂移。你通过在浏览器中直接运行的动手代码来练习 MLOps Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。

学习 MLOps Academy 需要有经验吗？

无需任何先前经验。CoddyKit 上的 MLOps Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。这是第 3 节课，共 4 节。

「世界在模型之下发生变化」课时需要多长时间？

大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。

我能在这节 MLOps Academy 课中编写并运行代码吗？

能。每节 MLOps Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。

此课程中的所有课时

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