Hi Kedro Community, We are trying to implement experiment tracking for our ml project with the help kedro-mlflow plugin. The model that we have is a bagged classifier model. When we run the training pipeline the model object is saved as mlflow.pyfunc.PyFuncModel and while doing the batch predictions for the new data we are getting the error "object of type PyFuncModel" has no len(). Is there a workaround for this ?

cc @Yolan Honoré-Rougé @Takieddine Kadiri batsignal

A few questions : • Is your model a custom mlflow model or a standard one (e.g. a sklearn model)? Can you show the code? • Are you using the same versions of mlflow /kedro/sklearn... For training and inference? Can you tell which versions? • Are you saving the model with kedro_mlflow.io.models.MlflowModelTrackingDataset in your catalog? Can you show the entry?

@Yolan Honoré-Rougé thanks for the response. 1. the model is bagged logistic regression model. 2. yes, using the same version. Building a POC using the official documentation and following the steps given in tutorial - https://github.com/Galileo-Galilei/kedro-mlflow-tutorial/blob/main/README.md 3. saving the model trained model as

aggregated_linear_classifier_model_mlflow:
  type: pickle.PickleDataset
  filepath: data/06_models/mlflow_implementation/aggregated_linear_classifier_model_mlflow.pkl

and picking the model as pipeline_inference_model

pipeline_inference_model:
  type: kedro_mlflow.io.models.MlflowModelTrackingDataset
  flavor: mlflow.pyfunc
  pyfunc_workflow: python_model
  artifact_path: mlflow_implementation
  run_id: f4d03b05236f4ae5927164a60ce86ed3

Mlflow cannot read a pickle directly, it needs its own format. You need to save AND load the model with the MlflowTrackingDataset

yes, using pipeline_ml_factory

So it's a different issue

yes, I do have preprocessing code to be bundled with the model

If you do kedro run --pipeline=inference, does it work? This does not use mlflow so if this does not work, the pb lies in your kedro pipeline

inference pipeline is working

Hum very weird

no worries, thanks for the responses. Sure, I will create one and share. Meanwhile will try to debug this more

Hi, do you have an update?

@Yolan Honoré-Rougé apologies, I missed out on your message yesterday. I simplified my approach and made couple of changes in the pipeline. 1. Instead of using bagged logistic regression model, I updated the code to use stand alone sklearn logistic regression model. 2. Instead of calculating predicted prob, I classified the predictions using model.predict After these changes the pipeline ran without any error Post some googling, we realised that we need to write a custom model class for this. Now trying to work upon this. Does this seem feasible to you ?

No problem. Actually pipeline_ml_favtory is supposed to work with any kedro pipeline (provide it has a single free input and a single output,which is a mlflow constraints) so I don't know what the problem is, but your initial workflow looks fine. What it does under the hood is precisely to create a custom model to create a mlflow model from kedro pipeline, and while it's of course possible to create your own, I don't think that will be easier than using the kedro-mlflow one.