---

name: app-deployment description: "Generate, inspect, serve locally, and publish Mercury prediction apps from trained MLJAR AutoML models." disable-model-invocation: true metadata: disco-role: operating license: MIT

App Deployment

Use this sub-skill when a task involves turning an already fitted supervised.AutoML model into a Mercury prediction app, inspecting the generated app workspace, previewing it locally, or preparing a guarded publish flow.

Route here for

• Generating app files with automl.app(path=..., overwrite=..., title=...).
• Inspecting generated files such as mljar_app.json, notebooks, automl.zip, config.toml, requirements.txt, and runtime.txt.
• Starting a local Mercury preview with automl.local_app() when Mercury is installed and the user accepts a foreground server process.
• Publishing with automl.publish_app(...) only after the user explicitly authorizes browser, network, and platform-auth actions.
• Diagnosing app workspace, Mercury startup, and publish failures.

Route elsewhere

• To ../training-core/ when the user still needs to train, configure, evaluate, or retrain an AutoML model.
• To ../artifacts-reports/ for interpreting saved model folders, leaderboards, structured reports, explainability outputs, or results_path artifacts outside the generated app workspace.
• To ../fairness-workflows/ for fairness metrics, sensitive features, and fairness dashboard/report interpretation.
• Exclude maintainer-only CI docs deployment, package release, and platform credential administration tasks.

Operating checklist

1. Confirm supervised imports and the model was fitted. App generation needs a valid results_path containing fitted AutoML artifacts.
2. Choose the deployment action:
   • app() for file generation only.
   • local_app() for a local Mercury server and browser preview.
   • publish_app() for the hosted platform workflow; require explicit authorization.
3. Decide output location and overwrite policy before writing app files. overwrite=True deletes an existing app workspace.
4. Inspect the generated mljar_app.json manifest before serving or publishing, especially supports.single_sample, feature count, and selected notebooks.
5. For local serving, install the generated app dependencies in a Python 3.10+ environment and ensure the mercury executable is on PATH.
6. For publishing, avoid embedding credentials in code. Use open_browser=False in non-interactive sessions and have the user open the printed login URL manually.

Read next

• App workflows for generation, local preview, manual hosting, and publish recipes.
• API reference for method signatures, return values, generated files, and state behavior.
• Troubleshooting for overwrite, unfitted model, Mercury, server logs, publish/auth/network, URL reuse, and wide dataset issues.
• scripts/generate_app_workspace_smoke.py for a safe synthetic helper that trains a tiny model and generates app files without starting Mercury or publishing.
• Root package assumptions: ../../references/package-overview.md; cross-cutting install/import issues: ../../references/troubleshooting.md.