MLOps Academy · 课时

记录预测结果以便后续分析

捕获特征和输出，并保存到预测存储中

记录预测结果以便后续分析 是 CoddyKit 上的免费 MLOps Academy 课时。这是第 2 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。

You Cannot Debug What You Did Not Save

If you never store what your model saw and said, you cannot diagnose it later. Prediction logging is the foundation of all analysis. 📝

What a Prediction Log Holds

A useful prediction log captures the input features, the model output, the model version, and a timestamp for every request.

Add a Request ID

Give each prediction a unique request ID. Later, when the true outcome arrives, you join it back to the original input by that ID.

Log Inputs and Outputs Together

Always store the features alongside the prediction. A score with no inputs cannot tell you why the model decided what it did.

record = {
    "request_id": rid,
    "features": features,
    "prediction": pred,
    "model_version": "v3",
}

Stamp the Model Version

Tag every log with the model version that produced it. After a rollout you can compare old and new behavior side by side.

Use Structured JSON

Write logs as structured JSON, not free text. Machines can then query, filter, and aggregate your predictions automatically.

The Prediction Store

Predictions usually flow into a prediction store: a database, warehouse, or data lake built for cheap, queryable history.

Log Asynchronously

Write logs asynchronously so storing a record never slows down the response your user is waiting for.

Mind Privacy

Logs may hold personal data, so apply privacy rules: hash identifiers, drop sensitive fields, and set a retention window.

Join Predictions to Outcomes

When real labels land, join them to the logs by request ID. That join is how you measure live accuracy after the fact.

Sample if Volume Is Huge

At massive scale, log a representative sample instead of every request to keep storage and cost under control.

Quick Check

Think about reconnecting a prediction to reality.

Recap

Log every prediction with its inputs, output, version, and request ID. That structured history is what every later analysis relies on. ✅

常见问题解答

「记录预测结果以便后续分析」课时是免费的吗？

是的 — 「记录预测结果以便后续分析」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。

「记录预测结果以便后续分析」这节课中我会学到什么？

捕获特征和输出，并保存到预测存储中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 2 节课，共 4 节。

「记录预测结果以便后续分析」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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