Merge pull request #223179 from santiagxf/santiagxf/mlflow-2.0-deprecation-models

Update how-to-manage-models-mlflow.md

Author: prmerger-automator[bot]

articles/machine-learning/how-to-manage-models-mlflow.md

@@ -76,10 +76,13 @@ client = mlflow.tracking.MlflowClient()
 The following sample prints all the model's names:
 
 ```python
-for model in client.list_registered_models():
+for model in client.search_registered_models():
     print(f"{model.name}")
 ```
 
+> [!NOTE]
+> __MLflow 2.0 advisory:__ In older versions of Mlflow (<2.0), use method `MlflowClient.list_registered_models()` instead.
+
 ### Getting specific versions of the model
 
 The command above will retrieve the model object which contains all the model versions. However, if you want to get the last registered model version of a given model, you can use `get_registered_model`:
@@ -158,7 +161,7 @@ model = mlflow.pyfunc.load_model(f"models:/{model_name}/Staging")
 
 ## Editing and deleting models
 
-Editing registered models is supported in both Mlflow and Azure ML, however, there are some differences between them that are important to notice:
+Editing registered models is supported in both Mlflow and Azure ML. However, there are some differences important to be noticed:
 
 > [!WARNING]
 > Renaming models is not supported in Azure Machine Learning as model objects are immmutable.
@@ -203,7 +206,7 @@ The MLflow client exposes several methods to retrieve and manage models. The fol
 | Registering models in MLflow format | **&check;** | **&check;** | **&check;** | **&check;** |
 | Registering models not in MLflow format |  |  | **&check;** | **&check;** |
 | Registering models from runs outputs/artifacts | **&check;** | **&check;**<sup>1</sup> | **&check;**<sup>2</sup> | **&check;** |
-| Registering models from runs outputs/artifacts in a different tracking server/workspace | **&check;** |  |  |  |
+| Registering models from runs outputs/artifacts in a different tracking server/workspace | **&check;** |  | **&check;**<sup>5</sup> | **&check;**<sup>5</sup> |
 | Listing registered models | **&check;** | **&check;** | **&check;** | **&check;** |
 | Retrieving details of registered model's versions | **&check;** | **&check;** | **&check;** | **&check;** |
 | Editing registered model's versions description | **&check;** | **&check;** | **&check;** | **&check;** |
@@ -221,9 +224,10 @@ The MLflow client exposes several methods to retrieve and manage models. The fol
 > - <sup>2</sup> Use URIs with format `azureml://jobs/<job-id>/outputs/artifacts/<path>`.
 > - <sup>3</sup> Registered models are immutable objects in Azure ML.
 > - <sup>4</sup> Use search box in Azure ML Studio. Partial match supported.
+> - <sup>5</sup> Use [registries](how-to-manage-registries.md).
 
 ## Next steps
 
 - [Logging MLflow models](how-to-log-mlflow-models.md)
 - [Query & compare experiments and runs with MLflow](how-to-track-experiments-mlflow.md)
-- [Guidelines for deploying MLflow models](how-to-deploy-mlflow-models.md)
+- [Guidelines for deploying MLflow models](how-to-deploy-mlflow-models.md)