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Dataset versioning
How to Manage Dataset Versions and Release Delivery
This guide shows how to freeze reviewed output into dataset versions, connect release delivery, and keep training and handoff lineage explainable.
8分dataset versionrelease deliverytraining lineage
Freeze only reviewed and approved output
A dataset version should explain what the team accepted at that point in time, not whatever happens to be latest in the project.
Freeze from approved submissions instead of work-in-progress results.
Record scope, count, and rule snapshot at freeze time.
Treat version freeze as the minimum delivery boundary.
Keep release, training, and delivery on one lineage
The point of dataset versioning is not the label itself, but the ability to explain which release and which training run came from which frozen source.
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Bind the release candidate to that version.
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Bind training or export tasks to the same version or source release.
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Publish delivery summary with the same lineage context.
Use naming and acceptance rules that survive handoff
If version names only make sense to the person who created them, delivery and procurement conversations will break later.
Include scope or milestone in the version label.
Write one short acceptance note per frozen version.
Let delivery pages show version, release, and artifact together.
FAQ
Should every training run bind a dataset version? If you want lineage to stay explainable, yes. Otherwise delivery and training context drift apart quickly.
What is the difference between dataset version and release? A dataset version freezes reviewed data. A release is the delivery surface built from that frozen version and its related artifacts.
次に学ぶおすすめ
初めてなら、まず注釈とエクスポートのガイドから始めてください。
チーム workflow を準備しているなら、コラボレーションとプラン選択ガイドへ進んでください。
workflow 全体を理解したいなら、その次に OpenClaw と学習ガイドへ進んでください。