Turn a custom model in the flow into a model object

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

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