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The Data Playground is an interactive exploration workspace. It loads a dataset (or subset) into an in‑memory session where you can poke at the data, run lightweight previews, and publish the result back as a new dataset or branch. It’s behind the playground.access permission, see Team & Access.

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When to use it

  • You just imported a dataset and want a feel for it before kicking off heavy scans.
  • You need to edit labels by hand on a small subset to seed a cleaner training run.
  • You want to propose a stratified split and visualize the class balance before saving it.
  • You want a quick bias peek without paying for the full bias_shortcut engine.

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Sessions

A session is one user’s working copy of a dataset, isolated from everyone else. Sessions live on the Playground home page (/playground). Each session has its own isolated workspace so multiple teammates can explore the same dataset in parallel without stepping on each other.

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Creating a session

1

Pick a source

From /playground click New session. Pick one of:
  • An existing dataset (whole, or filtered).
  • A new upload (zip / folder).
  • A pull from HuggingFace, Kaggle, or S3.
2

Wait for preparation

The session shows a streamed progress bar. You can cancel from the session card.
3

Open the editor

Once ready, the Playground opens with the data table, filters, and the analysis panels.

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What the editor gives you

The bias preview and embedding view are directional, they’re meant to point you at problems, not replace a full scan. Treat them like a sanity check.

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Saving your work

You have three ways to commit changes: Published datasets get a lineage edge playground_session linking them to the session they came from, so auditors can trace any hand‑edited samples back to who edited them.

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Common workflows

  1. Create a session against the dataset, no subsetting.
  2. Open the embedding view; look for tight unexpected clusters.
  3. Skim the distributions panel for class imbalance or weird size outliers.
  4. Close the session; no need to publish.
  1. Filter the table to the class you want to clean.
  2. Use multi‑select + bulk label flip on visually mislabeled samples.
  3. Publish as a new branch on the original dataset.
  4. Train a quick model on the cured branch as an A/B test.
  1. Open the splits panel and choose stratified by class.
  2. Eyeball the proposed sizes per class.
  3. Adjust the random seed if a class is too small in a split.
  4. Update cells to commit the split column back to the dataset’s branch.

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Sessions and cleanup

  • Long‑idle sessions time out and are cleaned up automatically. The exact threshold is set on your tenant (typically 24h of inactivity).
  • You can rename, cancel, retry, or delete any session from the Playground home page.
  • Deleting a session does not affect the dataset it was sourced from.