Datasets
A capability is only as good as the data behind it. Pradra Studio treats data as a first-class, versioned asset — never a loose file on someone's laptop.
What a dataset is
A dataset is a validated, versioned collection of the data you train and evaluate on. When you upload data (or a connector syncs it in), the platform:
- Validates it — checks structure and column types so problems surface early, before they cost a training run.
- Versions it — each upload becomes an immutable version with a fixed fingerprint.
- Keeps it — datasets are deprecated when retired, never silently deleted.
Because every version is immutable, a result can always be traced back to the exact data that produced it. Two people training against "version 3" are guaranteed to be using the same rows — and a model in production can always answer "what was I trained on?".
A dataset moves through: Uploaded → Validated → Versioned → Deprecated.
Working with datasets in the Studio
- Go to Datasets in the sidebar. The list shows every dataset in your workspace with its status and latest version.
- Click Create and upload a CSV. Validation runs automatically; a dataset that fails validation tells you what is wrong (missing headers, inconsistent types) instead of quietly accepting bad data.
- Open a dataset to see its detail page: version history, column statistics and distributions (so you can eyeball the data before trusting it), and the feature sets defined on top of it.
- Upload again to create the next version — the previous one stays exactly as it was, still referenced by any model trained on it.
Three ways data gets in
| Path | When to use it |
|---|---|
| Manual upload | One-off files, experiments, getting started. |
| Connector | The data lives in Postgres, MySQL, or S3 and should sync on a schedule. |
| Prediction feedback | Down the line, production predictions themselves become labeled data for retraining. |
All three land in the same place: a validated, versioned dataset.
Good habits
- Name datasets after what they contain, not after today's date — the version history carries the dates for you.
- Deprecate, don't delete. A deprecated dataset stops appearing in pickers but keeps every promise it made to the models trained on it.
- Check the column statistics on the detail page before training: a column that is 60% empty is easier to find now than after a disappointing model.
Next
- Shape the data for a model: Feature sets.
- Automate the inflow: Connectors.