Skip to content

Optimiser Apply

You've run the Optimiser and saved the results. Now you want to apply those results - the per-quote optimisation parameters (online mode) or the factor tables (ratebook mode) - to fresh data at deployment time.

When to use

Use this to apply saved optimisation results to fresh data - typically in your production pipeline. The Optimiser itself is run during development; Optimiser Apply loads the saved results at deployment time.

This node accepts a single input.

Config Description
sourceType Required. "file", "registered", or "run"
artifact_path Path to the saved optimiser artifact. Required when sourceType is "file".
registered_model Model registry name. Required when sourceType is "registered".
version Version or "latest". Required when sourceType is "registered".
experiment_id MLflow experiment ID. Required when sourceType is "run".
run_id MLflow run ID. Required when sourceType is "run".
version_column Column name for version tracking. Defaults to "__optimiser_version__".

Example

{
  "sourceType": "registered",
  "registered_model": "motor_pricing_optimiser",
  "version": "latest"
}

This loads the latest version of the motor_pricing_optimiser model from the registry and applies it to incoming data.

What it does to the data

In online mode, the node adds the optimal price for each quote as a new column. In ratebook mode, it applies the optimised factor tables - each quote receives the adjusted factors. The output includes all columns from the input plus the optimisation results.

Source types

Source type Use case Required config
file Local development - loads from a path on disk artifact_path
registered Production - loads from the MLflow model registry registered_model, version
run Loads from a specific training experiment run experiment_id, run_id

See also: