client.register

For more information, see Registering a Model.

Input Parameter	Type	Default	Description
project_id	str	None	The unique identifier for the project.
model_id	str	None	A unique identifier for the model. Must be a lowercase string between 2-30 characters containing only alphanumeric characters and underscores. Additionally, it must not start with a numeric character.
dataset_id	str	None	The unique identifier for the dataset.
model_info	fdl.ModelInfo	None	A fdl.ModelInfo() object containing information about the model.
deployment	Optional [fdl.core_objects.DeploymentOptions]	None	A DeploymentOptions object containing information about the model deployment.
cache_global_impact_importance	Optional [bool]	True	If True, global feature impact and global feature importance will be precomputed and cached when the model is registered.
cache_global_pdps	Optional [bool]	False	If True, global partial dependence plots will be precomputed and cached when the model is registered.
cache_dataset	Optional [bool]	True	If True, histogram information for the baseline dataset will be precomputed and cached when the model is registered.

PROJECT_ID = 'example_project'
DATASET_ID = 'example_dataset'
MODEL_ID = 'example_model'

dataset_info = client.get_dataset_info(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID
)

model_task = fdl.ModelTask.BINARY_CLASSIFICATION
model_target = 'target_column'
model_output = 'output_column'
model_features = [
    'feature_1',
    'feature_2',
    'feature_3'
]

model_info = fdl.ModelInfo.from_dataset_info(
    dataset_info=dataset_info,
    target=model_target,
    outputs=[model_output],
    model_task=model_task
)

client.register_model(
    project_id=PROJECT_ID,
    dataset_id=DATASET_ID,
    model_id=MODEL_ID,
    model_info=model_info
)

Return Type	Description
str	A message confirming that the model was registered.

'Model successfully registered on Fiddler. \n Visit https://app.fiddler.ai/projects/example_project'