Automatically log an inference after running the training pipeline

For consistency, you may want to log an inference pipeline (including some data preprocessing and prediction post processing) after you ran a training pipeline, with all the artifacts newly generated (the new model, encoders, vectorizers…).

Getting started

  1. Install kedro-mlflow MlflowHook (this is done automatically if you have installed kedro-mlflow in a kedro>=0.16.5 project)

  2. Turn your training pipeline in a PipelineML object with pipeline_ml_factory function in your

    # for kedro>=0.17.2 ( for ``kedro>=0.16.5, <0.17.2)
    from kedro_mlflow_tutorial.pipelines.ml_app.pipeline import create_ml_pipeline
    def register_pipelines(self) -> Dict[str, Pipeline]:
        ml_pipeline = create_ml_pipeline()
        training_pipeline_ml = pipeline_ml_factory(
                    "python": 3.10,
                    "dependencies": [f"kedro_mlflow_tutorial=={PROJECT_VERSION}"],
        return {"training": training_pipeline_ml}
  3. Persist your artifacts locally in the catalog.yml

    type: pickle.PickleDataset  # <- This must be any Kedro Dataset other than "MemoryDataset"
    filepath: data/06_models/label_encoder.pkl  # <- This must be a local path, no matter what is your mlflow storage (S3 or other)
  4. Launch your training pipeline:

    kedro run --pipeline=training

    The inference pipeline will automagically be logged as a mlflow model at the end!

  5. Go to the UI, retrieve the run id of your “inference pipeline” model and use it as you want, e.g. in the catalog.yml:

    # catalog.yml
    flavor: mlflow.pyfunc
    pyfunc_workflow: python_model
    artifact_path: kedro_mlflow_tutorial  # the name of your mlflow folder = the model_name in pipeline_ml_factory
    run_id: <your-run-id>  

Complete step by step demo project with code

A step by step tutorial with code is available in the kedro-mlflow-tutorial repository on github.

You have also other resources to understand the rationale:


You can find more about the motivations in