MLOps Academy · 课时

阶段：摄取、准备、训练、评估

将训练拆分为清晰且可缓存的步骤

阶段：摄取、准备、训练、评估 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。

From One Big Script to Stages

A single train.py that does everything is hard to reuse and slow to debug. Splitting it into stages makes each step clear and independently runnable. 🧩

The Four Classic Stages

Most training pipelines follow the same shape: ingest, prep, train, eval. Each stage takes inputs and produces outputs the next one reads.

Stage 1: Ingest

The ingest stage pulls raw data from its source, like a database or bucket, and writes it to a known local path. Nothing is cleaned yet.

import pandas as pd
df = pd.read_csv("s3://bucket/raw.csv")
df.to_parquet("data/raw.parquet")

Stage 2: Prep

The prep stage cleans and transforms raw data into model-ready features. It handles missing values, encoding, and splitting into train and test sets.

Stage 3: Train

The train stage reads prepared features and fits the model, then saves the fitted artifact to disk for the next stage to score.

model.fit(X_train, y_train)
joblib.dump(model, "models/model.pkl")

Stage 4: Eval

The eval stage loads the saved model, scores it on the held-out test set, and writes metrics like accuracy to a file you can track.

score = model.score(X_test, y_test)
json.dump({"accuracy": score}, open("metrics.json", "w"))

Stages Pass Files, Not Variables

Stages talk to each other through files on disk, not in-memory variables. That decoupling is what lets you re-run one stage without the others.

Why Stages Are Cacheable

Because each stage has fixed inputs and outputs, a tool can cache its result. If the inputs did not change, the stage is skipped entirely.

Each Stage Is Its Own Script

Give every stage one small script with a clear job. ingest.py, prep.py, train.py, eval.py read inputs and write outputs, nothing more.

Stages Form a DAG

Stages connect into a DAG, a directed graph where each step depends on the ones before it. Eval depends on train, train on prep, prep on ingest.

Clear Stages Make Debugging Easy

When a run fails, distinct stages tell you exactly where it broke. You can re-run just the failed stage instead of the whole pipeline.

Quick Check

Let us check how stages communicate.

Recap: Ingest, Prep, Train, Eval

You learned to split training into four cacheable stages that pass data through files. Clear stages mean faster reruns and far easier debugging. 🚀

常见问题解答

「阶段：摄取、准备、训练、评估」课时是免费的吗？

是的 — 「阶段：摄取、准备、训练、评估」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。

「阶段：摄取、准备、训练、评估」这节课中我会学到什么？

将训练拆分为清晰且可缓存的步骤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。

「阶段：摄取、准备、训练、评估」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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