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 دقائقdataset 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.

01

Create the dataset version after approval.

02

Bind the release candidate to that version.

03

Bind training or export tasks to the same version or source release.

04

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.

خطوات التعلم التالية المقترحة

إذا كنت جديدًا، فابدأ بأدلة الوسم والتصدير.
إذا كنت تُعد workflows للفريق، فانتقل بعد ذلك إلى أدلة التعاون واختيار الخطة.
إذا كنت تريد workflow الكامل، فانتقل بعد ذلك إلى أدلة OpenClaw والتدريب.
الخطوة التالية

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