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按计划进行批量评分

对整个数据集进行预测并保存结果

按计划进行批量评分 是 CoddyKit 上的免费 MLOps Academy 课时。这是第 1 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。

What Batch Scoring Is

Batch scoring runs your model over a whole dataset at once, on a schedule, and saves every prediction for later use. 📦

No User Is Waiting

In batch mode nobody waits for a live answer. The job runs offline, so a few minutes or hours of runtime is perfectly fine.

A Classic Example

Think of scoring churn risk for every customer each night. One job reads the table, predicts for all rows, and writes the results back.

Read, Predict, Write

Every batch job follows the same shape: load the input data, call predict on it, then store the output where consumers can read it.

df = load_data()
preds = model.predict(df)
save(df.assign(score=preds))

Predict on Many Rows

Models love arrays. Passing a whole DataFrame to predict is far faster than looping one row at a time, because work is vectorized.

preds = model.predict(df[features])

Where Results Live

Batch predictions land in a table, file, or warehouse. Apps then just look up the stored score instead of calling the model live.

Run It on a Schedule

A scheduler like cron or Airflow fires the job at a fixed time. This cadence can be hourly, nightly, or weekly to fit how fast data changes.

# crontab: run every day at 2am
0 2 * * * python score_batch.py

Why Batch Is Cheap

You spin up compute, score everything, then shut it down. This burst pattern means you pay only for the minutes the job actually runs.

Freshness Is the Trade

The cost of batch is staleness. A score from last night may be hours old, so batch fits cases where slightly stale answers are acceptable.

Idempotent and Safe

Design the job so re-running it gives the same result. An idempotent job can be retried after a failure without creating duplicates.

When to Pick Batch

Choose batch when predictions cover known entities, freshness in hours is fine, and you want simplicity and low cost over instant answers.

Quick Check

What is the main downside of batch scoring?

Recap

Batch scoring predicts over a full dataset on a schedule, stores results for fast lookup, and trades freshness for low cost and simplicity.

常见问题解答

「按计划进行批量评分」课时是免费的吗？

是的 — 「按计划进行批量评分」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。

「按计划进行批量评分」这节课中我会学到什么？

对整个数据集进行预测并保存结果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 1 节课，共 4 节。

「按计划进行批量评分」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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