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Important
This article references Azure Machine Learning SDK v1. SDK v1 is deprecated as of March 31, 2025. Support for it ends on June 30, 2026. Your existing workflows that use SDK v1 continue to operate after the end-of-support date, but they could be exposed to security risks or breaking changes. Transition to SDK v2 before June 30, 2026. For more information, see What is Azure Machine Learning CLI and Python SDK v2?
This article provides a comparison of scenarios in SDK v1 and SDK v2.
Create model
SDK v1
from azureml.core.model import Model # Register model model = Model.register(ws, model_name="local-file-example", model_path="mlflow-model/model.pkl")SDK v2
from azure.ai.ml.entities import Model from azure.ai.ml.constants import AssetTypes file_model = Model( path="mlflow-model/model.pkl", type=AssetTypes.CUSTOM_MODEL, name="local-file-example", description="Model created from local file.", stage="Development" # Optional lifecycle stage: Development, Production, or Archived ) ml_client.models.create_or_update(file_model)
Use model in an experiment or job
SDK v1
model = run.register_model(model_name='run-model-example', model_path='outputs/model/') print(model.name, model.id, model.version, sep='\t')SDK v2
from azure.ai.ml.entities import Model from azure.ai.ml.constants import AssetTypes run_model = Model( path="azureml://jobs/$RUN_ID/outputs/artifacts/paths/model/", name="run-model-example", description="Model created from run.", type=AssetTypes.CUSTOM_MODEL ) ml_client.models.create_or_update(run_model)
For more information about models, see Work with models in Azure Machine Learning.
Mapping of key functionality in SDK v1 and SDK v2
| Functionality in SDK v1 | Rough mapping in SDK v2 |
|---|---|
| Model.register | ml_client.models.create_or_update |
| run.register_model | ml_client.models.create_or_update |
| Model.deploy | ml_client.begin_create_or_update(blue_deployment) |
Next steps
For more information, see the following documentation: