Turn a custom model in the flow into a model object

Tanguy

---

I was told that it was possible to turn a custom trained model, typically stored in a managed folder, into a visual model object in the flow.

Currently our flow looks like this:

but we would like to see something like this in the flow:

I couldn’t find any documentation on how to do this, so I’m turning to the Dataiku community for help

JordanB

Hi @tanguy
,

There is example code in the link I provided above (see here).

I've added the code below for reference:

import dataiku

 # if using API from inside DSS
client = dataiku.api_client()

project = client.get_project("PROJECT_ID")

# 1. Create DSS Saved Model
saved_model = project.create_mlflow_pyfunc_model(name, prediction_type)

# 2. Load the MLflow Model as a new version of DSS Saved Model
## either from DSS host local filesystem:
mlflow_version = saved_model.import_mlflow_version_from_path("version_id", model_directory, 'code-environment-to-use')
## or from a DSS managed folder:
mlflow_version = saved_model.import_mlflow_version_from_managed_folder('version_id', 'managed_folder_id', path_of_model, 'code-environment-to-use')

# 3. Evaluate the saved model version
# (Optional, only for regression or classification models with tabular input data, mandatory to have access to the saved model performance tab)
mlflow_version.set_core_metadata(target_column, classes, evaluation_dataset_name)
mlflow_version.evaluate(evaluation_dataset_name)

Thanks,

Jordan

JordanB

Hi @tanguy
,

You can import MLFlow models as saved models to be deployed to your flow: https://doc.dataiku.com/dss/latest/mlops/mlflow-models/importing.html

If your model is not currently saved as an MLFlow model, you can do so within your python recipe and load it into a managed folder. Then, use the DSS Python APIs to create your DSS saved model and deploy it.

Thanks,

Jordan

Tanguy

Thanks Jordan. Is there any documentation or example regarding this step?

use the DSS Python APIs to create your DSS saved model and deploy it.