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ML Generator View

Overview

ML Generator View is the project-level workspace for inspecting persisted ML generator results from executed training runs, comparing versions, and promoting a validated default version for downstream generation flows.

Open it from the top navigation via ML-Gen.

Only models with completed/persisted training appear with versions and statistics in this view.

ML Generator View
ML Generator View: generator list, version panel, and quality metrics

Main Areas

Left Pane: Generator List

  • Search generators by name.
  • Select a generator to load its details.
  • See current default version and update timestamp.

Center Pane: Detail and Quality

  • Headline cards:
  • dataset sizes (train/holdout/synthetic),
  • utility score,
  • privacy score,
  • overall status.
  • KPI metrics (accuracy, univariate/bivariate/trivariate signals, NN distances).
  • Top drift table with column-wise KL/JS drift indicators.

Right Pane: Versions and Actions

  • Choose a specific version.
  • Set selected version as default.
  • Delete selected version.

Top Actions

  • Full report: open detailed QA report view.
  • Export: export QA report artifact if available.
  • Model metadata: open model metadata for selected version.
  • Clear selection: reset current detail selection.

Relationship to Database View and DSL

Typical flow:

  1. Create ML training model artifacts from Database View → Create ML.
  2. Execute the generated DSL model so <ml-train> runs and persists model versions.
  3. Validate outcome in ML Generator View.
  4. Reuse approved model version in DSL source with ml://....

Note

Training can be long-running. Runtime sizing should match workload (rows/features), and some setups may require higher-end resources, including GPU-enabled workers.

Example:

1
<generate name="accounts_stage" source="ml://accounts_model" count="1000" target="mapping" />

For full flow guidance, see ML Generator from Database Metadata.