Update concept-mlflow.md

Author: santiagxf

articles/machine-learning/concept-mlflow.md

@@ -72,21 +72,6 @@ You can submit training jobs to Azure Machine Learning using [MLflow Projects](h
 
 Learn more at [Train ML models with MLflow projects and Azure Machine Learning (preview)](how-to-train-mlflow-projects.md).
 
-## Example notebooks
-
-If you are getting started with MLflow in Azure Machine Learning, we recommend you to explore the [notebooks examples about how to user MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/readme.md):
-
-* [Training and tracking a classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb): Demonstrates how to track experiments using MLflow, log models and combine multiple flavors into pipelines.
-* [Training and tracking a classifier with MLflow using Service Principal authentication](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/train-with-mlflow/xgboost_service_principal.ipynb): Demonstrate how to track experiments using MLflow from compute that is running outside Azure ML and how to authenticate against Azure ML services using a Service Principal.
-* [Hyper-parameters optimization using child runs with MLflow and HyperOpt optimizer](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/train-with-mlflow/xgboost_nested_runs.ipynb): Demonstrate how to use child runs in MLflow to do hyper-parameter optimization for models using the popular library HyperOpt. It shows how to transfer metrics, params and artifacts from child runs to parent runs.
-* [Logging models instead of assets with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/logging-models/logging_model_with_mlflow.ipynb): Demonstrates how to use the concept of model instead of artifacts with MLflow, including how to construct custom models.
-* [Manage experiments and runs with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/run-history/run_history.ipynb): Demonstrates how to query experiments, runs, metrics, parameters and artifacts from Azure ML using MLflow.
-* [Manage models registries with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/model-management/model_management.ipynb): Demonstrates how to manage models in registries using MLflow.
-* [No-code deployment with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/no-code-deployment/deploying_with_mlflow.ipynb): Demonstrates how to deploy models in MLflow format to the different deployment target in Azure ML.
-* [Training models in Azure Databricks and deploying them on Azure ML with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb): Demonstrates how to train models in Azure Databricks and deploy them in Azure ML. It also includes how to handle cases where you also want to track the experiments with the MLflow instance in Azure Databricks.
-* [Migrating models with scoring scripts to MLflow format](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/migrating-scoring-to-mlflow/scoring_to_mlmodel.ipynb): Demonstrates how to migrate models with scoring scripts to no-code-deployment with MLflow.
-* [Using MLflow REST with Azure ML](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/using-rest-api/using_mlflow_rest_api.ipynb): Demonstrates how to work with MLflow REST API when connected to Azure ML.
-
 ## MLflow SDK, Azure ML v2 and Azure ML Studio capabilities
 
 The following table shows which operations are supported by each of the tools available in the ML lifecycle.
@@ -110,8 +95,17 @@ The following table shows which operations are supported by each of the tools av
 > - <sup>3</sup> View [Manage models registries in Azure Machine Learning with MLflow](how-to-manage-models-mlflow.md) for details.
 > - <sup>4</sup> View [Deploy MLflow models to Azure Machine Learning](how-to-deploy-mlflow-models.md) for details. Deployment of MLflow models to batch inference using the MLflow SDK is not possible by the moment.
 
+## Example notebooks
+
+If you are getting started with MLflow in Azure Machine Learning, we recommend you to explore the [notebooks examples about how to user MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/readme.md):
 
-## Next steps
-* [Track ML models with MLflow and Azure Machine Learning CLI v2](how-to-use-mlflow-cli-runs.md)
-* [Convert your custom model to MLflow model format for no code deployments](how-to-convert-custom-model-to-mlflow.md)
-* [Deploy MLflow models](how-to-deploy-mlflow-models.md)
+* [Training and tracking a classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb): Demonstrates how to track experiments using MLflow, log models and combine multiple flavors into pipelines.
+* [Training and tracking a classifier with MLflow using Service Principal authentication](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/train-with-mlflow/xgboost_service_principal.ipynb): Demonstrate how to track experiments using MLflow from compute that is running outside Azure ML and how to authenticate against Azure ML services using a Service Principal.
+* [Hyper-parameters optimization using child runs with MLflow and HyperOpt optimizer](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/train-with-mlflow/xgboost_nested_runs.ipynb): Demonstrate how to use child runs in MLflow to do hyper-parameter optimization for models using the popular library HyperOpt. It shows how to transfer metrics, params and artifacts from child runs to parent runs.
+* [Logging models instead of assets with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/logging-models/logging_model_with_mlflow.ipynb): Demonstrates how to use the concept of models instead of artifacts with MLflow, including how to construct custom models.
+* [Manage experiments and runs with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/run-history/run_history.ipynb): Demonstrates how to query experiments, runs, metrics, parameters and artifacts from Azure ML using MLflow.
+* [Manage models registries with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/model-management/model_management.ipynb): Demonstrates how to manage models in registries using MLflow.
+* [No-code deployment with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/no-code-deployment/deploying_with_mlflow.ipynb): Demonstrates how to deploy models in MLflow format to the different deployment target in Azure ML.
+* [Training models in Azure Databricks and deploying them on Azure ML with MLflow](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb): Demonstrates how to train models in Azure Databricks and deploy them in Azure ML. It also includes how to handle cases where you also want to track the experiments with the MLflow instance in Azure Databricks.
+* [Migrating models with scoring scripts to MLflow format](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/migrating-scoring-to-mlflow/scoring_to_mlmodel.ipynb): Demonstrates how to migrate models with scoring scripts to no-code-deployment with MLflow.
+* [Using MLflow REST with Azure ML](https://github.com/Azure/azureml-examples/blob/main/notebooks/using-mlflow/using-rest-api/using_mlflow_rest_api.ipynb): Demonstrates how to work with MLflow REST API when connected to Azure ML.