Update concept-mlflow.md

Author: santiagxf

articles/machine-learning/concept-mlflow.md

@@ -49,8 +49,8 @@ With MLflow Tracking you can connect Azure Machine Learning as the backend of yo
 * [Track Azure Synapse Analytics ML experiments](how-to-use-mlflow-azure-databricks.md) with MLflow in Azure Machine Learning.
 
 > [!IMPORTANT]
-> - MLflow in R support is limited to tracking experiment's metrics, parameters and models on Azure Machine Learning jobs. RStudio or Jupyter Notebooks with R kernels are not supported. Model registries are not supported using the MLflow R SDK. As an alternative, use Azure ML CLI or Azure ML studio for model registration and management. View the following [R example about using the MLflow tracking client with Azure Machine Learning](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/single-step/r).
-> - MLflow in Java support is limited to tracking experiment's metrics and parameters on Azure Machine Learning jobs. Artifacts and models can't be tracked using the MLflow Java SDK. View the following [Java example about using the MLflow tracking client with the Azure Machine Learning](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/single-step/java/iris).
+> - MLflow in R support is limited to tracking experiment's metrics, parameters and models on Azure Machine Learning jobs. Interactive training on RStudio or Jupyter Notebooks with R kernels is not supported. Model management and registration is not supported using the MLflow R SDK. As an alternative, use Azure ML CLI or Azure ML studio for model registration and management. View the following [R example about using the MLflow tracking client with Azure Machine Learning](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/single-step/r).
+> - MLflow in Java support is limited to tracking experiment's metrics and parameters on Azure Machine Learning jobs. Artifacts and models can't be tracked using the MLflow Java SDK. As an alternative, use the `Outputs` folder in jobs along with the method `mlflow.save_model` to save models (or artifacts) you want to capture. View the following [Java example about using the MLflow tracking client with the Azure Machine Learning](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/single-step/java/iris).
 
 To learn how to use MLflow to query experiments and runs in Azure Machine Learning, see [Manage experiments and runs with MLflow](how-to-track-experiments-mlflow.md)