Deployments
A deployment exposes one target of a finished analysis as an authenticated REST endpoint. Deploying does not recompute or copy anything — the endpoint reads the model your analysis already discovered and evaluates it in-process, so predictions are fast and always come from exactly the model you reviewed.
Creating a deployment
Open the notebook of a finished analysis and use the Deploy as REST endpoint action in the notebook header. Pick the target you want to serve and, optionally, a name. The dialog then shows the endpoint URL, the feature keys the model expects, the prediction interval derived from held-out data, and a copyable example call. All your deployments are listed on the Deployments page, where you can inspect each one, try it interactively, and delete it again.
The predict endpoint
Send a POST request with one row of feature values. Authenticate with a
dfat_ API token minted in Project settings → API Tokens (a signed-in session token works too) — see API Authentication. curl
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JavaScript
The response contains the predicted value, a 95% prediction interval, and a
predictionId you can use later to report the realised outcome: Response
The interval is computed from the residuals of held-out rows during the analysis (
method: "empirical_p5_p95"). When those residual statistics are not available, confidence is null. Numeric-looking strings in features are coerced to numbers; other strings are passed through unchanged. Closing the loop: record outcomes
Every prediction is logged. Once the real value is known, report it back with the
predictionId from the predict response: Record an outcome
Recorded outcomes feed the per-deployment performance report — mean absolute error, R², and the recent predictions with their actuals — shown in the expanded row on the Deployments page and available as
GET /api/v1/deployments/<deploymentId>/performance. MAE and R² appear once at least two outcomes have been recorded. You can also record outcomes directly in the performance table of the Deployments page. Scope and behavior
A deployment serves exactly one target of one analysis and is private to your account. Deleting a deployment removes the endpoint; the analysis and its model are untouched. The prediction is always a single numeric value per call, with one feature row per request.