Merge pull request #217993 from msakande/replace-link-to-v1-article

replace v1 link

Author: BCS2022

articles/machine-learning/concept-endpoints.md

@@ -58,7 +58,7 @@ Create and manage batch and online endpoints with multiple developer tools:
 
 **Online endpoints** are endpoints that are used for online (real-time) inferencing. Compared to **batch endpoints**, **online endpoints** contain **deployments** that are ready to receive data from clients and can send responses back in real time.
 
-The following diagram shows an online endpoint that has two deployments, 'blue' and 'green'. The blue deployment uses VMs with a CPU SKU, and runs v1 of a model. The green deployment uses VMs with a GPU SKU, and uses v2 of the model. The endpoint is configured to route 90% of incoming traffic to the blue deployment, while green receives the remaining 10%.
+The following diagram shows an online endpoint that has two deployments, 'blue' and 'green'. The blue deployment uses VMs with a CPU SKU, and runs version 1 of a model. The green deployment uses VMs with a GPU SKU, and uses version 2 of the model. The endpoint is configured to route 90% of incoming traffic to the blue deployment, while green receives the remaining 10%.
 
 :::image type="content" source="media/concept-endpoints/endpoint-concept.png" alt-text="Diagram showing an endpoint splitting traffic to two deployments.":::
 
@@ -209,13 +209,13 @@ You can [override compute resource settings](batch-inference/how-to-use-batch-en
 
 You can use the following options for input data when invoking a batch endpoint:
 
-- Cloud data - Either a path on Azure Machine Learning registered datastore, a reference to Azure Machine Learning registered V2 data asset, or a public URI. For more information, see [Connect to data with the Azure Machine Learning studio](v1/how-to-connect-data-ui.md)
-- Data stored locally - it will be automatically uploaded to the Azure ML registered datastore and passed to the batch endpoint.
+- Cloud data: Either a path on Azure Machine Learning registered datastore, a reference to Azure Machine Learning registered V2 data asset, or a public URI. For more information, see [Data in Azure Machine Learning](concept-data.md).
+- Data stored locally: The data will be automatically uploaded to the Azure ML registered datastore and passed to the batch endpoint.
 
 > [!NOTE]
-> - If you are using existing V1 FileDataset for batch endpoint, we recommend migrating them to V2 data assets and refer to them directly when invoking batch endpoints. Currently only data assets of type `uri_folder` or `uri_file` are supported. Batch endpoints created with GA CLIv2 (2.4.0 and newer) or GA REST API (2022-05-01 and newer) will not support V1 Dataset.
-> - You can also extract the URI or path on datastore extracted from V1 FileDataset by using `az ml dataset show` command with `--query` parameter and use that information for invoke.
-> - While Batch endpoints created with earlier APIs will continue to support V1 FileDataset, we will be adding further V2 data assets support with the latest API versions for even more usability and flexibility. For more information on V2 data assets, see [Work with data using SDK v2](how-to-read-write-data-v2.md). For more information on the new V2 experience, see [What is v2](concept-v2.md).
+> - If you're using existing V1 FileDatasets for batch endpoints, we recommend migrating them to V2 data assets. You can then refer to the V2 data assets directly when invoking batch endpoints. Currently, only data assets of type `uri_folder` or `uri_file` are supported. Batch endpoints created with GA CLIv2 (2.4.0 and newer) or GA REST API (2022-05-01 and newer) will not support V1 Datasets.
+> - You can also extract the datastores' URI or path from V1 FileDatasets. For this, you'll use the `az ml dataset show` command with the `--query` parameter and use that information for invoke.
+> - While batch endpoints created with earlier APIs will continue to support V1 FileDatasets, we'll be adding more support for V2 data assets in the latest API versions for better usability and flexibility. For more information on V2 data assets, see [Work with data using SDK v2](how-to-read-write-data-v2.md). For more information on the new V2 experience, see [What is v2](concept-v2.md).
 
 For more information on supported input options, see [Accessing data from batch endpoints jobs](batch-inference/how-to-access-data-batch-endpoints-jobs.md).
 

articles/machine-learning/concept-mlflow.md

@@ -94,18 +94,18 @@ The following table shows which operations are supported by each of the tools av
 ## Example notebooks
 
 
-If you're getting started with MLflow in Azure Machine Learning, we recommend that you explore the [notebook examples about how to use MLflow](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/readme.md):
-
-* [Training and tracking an XGBoost classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb): Demonstrates how to track experiments by using MLflow, log models, and combine multiple flavors into pipelines.
-* [Training and tracking an XGBoost classifier with MLflow using service principal authentication](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/train-with-mlflow/xgboost_service_principal.ipynb): Demonstrates how to track experiments by using MLflow from compute that's running outside Azure Machine Learning. It shows how to authenticate against Azure Machine Learning services by using a service principal.
-* [Hyper-parameter optimization using Hyperopt and nested runs in MLflow](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/train-with-mlflow/xgboost_nested_runs.ipynb): Demonstrates how to use child runs in MLflow to do hyper-parameter optimization for models by using the popular library Hyperopt. It shows how to transfer metrics, parameters, and artifacts from child runs to parent runs.
-* [Logging models with MLflow](https://github.com/Azure/azureml-examples/blob/main/v1/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 runs and experiments with MLflow](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/run-history/run_history.ipynb): Demonstrates how to query experiments, runs, metrics, parameters, and artifacts from Azure Machine Learning by using MLflow.
-* [Manage model registries with MLflow](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/model-management/model_management.ipynb): Demonstrates how to manage models in registries by using MLflow.
-* [Deploying models with MLflow](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/no-code-deployment/deploying_with_mlflow.ipynb): Demonstrates how to deploy no-code models in MLflow format to a deployment target in Azure Machine Learning.
-* [Training models in Azure Databricks and deploying them on Azure Machine Learning](https://github.com/Azure/azureml-examples/blob/main/v1/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 Machine Learning. 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 a scoring script to MLflow](https://github.com/Azure/azureml-examples/blob/main/v1/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 Machine Learning](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/using-rest-api/using_mlflow_rest_api.ipynb): Demonstrates how to work with the MLflow REST API when you're connected to Azure Machine Learning.
+If you're getting started with MLflow in Azure Machine Learning, we recommend that you explore the [notebook examples about how to use MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/readme.md):
+
+* [Training and tracking an XGBoost classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb): Demonstrates how to track experiments by using MLflow, log models, and combine multiple flavors into pipelines.
+* [Training and tracking an XGBoost classifier with MLflow using service principal authentication](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/train-with-mlflow/xgboost_service_principal.ipynb): Demonstrates how to track experiments by using MLflow from compute that's running outside Azure Machine Learning. It shows how to authenticate against Azure Machine Learning services by using a service principal.
+* [Hyper-parameter optimization using Hyperopt and nested runs in MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/train-with-mlflow/xgboost_nested_runs.ipynb): Demonstrates how to use child runs in MLflow to do hyper-parameter optimization for models by using the popular library Hyperopt. It shows how to transfer metrics, parameters, and artifacts from child runs to parent runs.
+* [Logging models with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/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 runs and experiments with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/run-history/run_history.ipynb): Demonstrates how to query experiments, runs, metrics, parameters, and artifacts from Azure Machine Learning by using MLflow.
+* [Manage model registries with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/model-management/model_management.ipynb): Demonstrates how to manage models in registries by using MLflow.
+* [Deploying models with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/no-code-deployment/deploying_with_mlflow.ipynb): Demonstrates how to deploy no-code models in MLflow format to a deployment target in Azure Machine Learning.
+* [Training models in Azure Databricks and deploying them on Azure Machine Learning](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb): Demonstrates how to train models in Azure Databricks and deploy them in Azure Machine Learning. 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 a scoring script to MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/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 Machine Learning](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/using-rest-api/using_mlflow_rest_api.ipynb): Demonstrates how to work with the MLflow REST API when you're connected to Azure Machine Learning.
 
 ## Next steps
 

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

@@ -53,7 +53,7 @@ accuracy = accuracy_score(y_test, y_pred)
 ```
 
 > [!TIP]
-> If you are using Machine Learning pipelines, like for instance [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html), use the `autolog` functionality of that flavor for logging models. Models are automatically logged when the `fit()` method is called on the pipeline object. The notebook [Training and tracking an XGBoost classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb) demonstrates how to log a model with preprocessing using pipelines.
+> If you are using Machine Learning pipelines, like for instance [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html), use the `autolog` functionality of that flavor for logging models. Models are automatically logged when the `fit()` method is called on the pipeline object. The notebook [Training and tracking an XGBoost classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb) demonstrates how to log a model with preprocessing using pipelines.
 
 ## Logging models with a custom signature, environment or samples
 

articles/machine-learning/how-to-track-experiments-mlflow.md

@@ -245,10 +245,10 @@ To compare and evaluate the quality of your jobs and models in AzureML Studio, u
 :::image type="content" source="media/how-to-track-experiments-mlflow/compare.gif" alt-text="Screenshot of the preview panel showing how to compare jobs and models in AzureML studio.":::
 
 
-The [MLflow with Azure ML notebooks](https://github.com/Azure/azureml-examples/tree/main/v1/notebooks/using-mlflow) demonstrate and expand upon concepts presented in this article.
+The [MLflow with Azure ML notebooks](https://github.com/Azure/azureml-examples/tree/main/sdk/python/using-mlflow) demonstrate and expand upon concepts presented in this article.
 
-  * [Training and tracking a classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/v1/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.
-  * [Manage experiments and runs with MLflow](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/run-history/run_history.ipynb): Demonstrates how to query experiments, runs, metrics, parameters and artifacts from Azure ML using MLflow.
+  * [Training and tracking a classifier with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/train-with-mlflow/xgboost_classification_mlflow.ipynb): Demonstrates how to track experiments using MLflow, log models and combine multiple flavors into pipelines.
+  * [Manage experiments and runs with MLflow](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/run-history/run_history.ipynb): Demonstrates how to query experiments, runs, metrics, parameters and artifacts from Azure ML using MLflow.
 
 
 ## Support matrix for querying runs and experiments

articles/machine-learning/how-to-train-mlflow-projects.md

@@ -10,7 +10,7 @@ ms.service: machine-learning
 ms.subservice: core
 ms.date: 11/04/2022
 ms.topic: conceptual
-ms.custom: how-to, devx-track-python, sdkv1, event-tier1-build-2022
+ms.custom: how-to, devx-track-python, sdkv2, event-tier1-build-2022
 ---
 
 # Train ML models with MLflow Projects and Azure Machine Learning (Preview)
@@ -215,7 +215,7 @@ If you don't plan to use the logged metrics and artifacts in your workspace, the
 
 1. In the Azure portal, select **Resource groups** on the far left.
 
-   ![Delete in the Azure portal](./v1/media/how-to-use-mlflow/delete-resources.png)
+    :::image type="content" source="media/how-to-use-mlflow-azure-databricks/delete-resources.png" alt-text="Image showing how to delete an Azure resource group.":::    
 
 1. From the list, select the resource group you created.
 

articles/machine-learning/how-to-use-mlflow-azure-databricks.md

@@ -33,7 +33,7 @@ In this article, you will learn:
 
 ### Example notebooks
 
-The [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/v1/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 and models with the MLflow instance in Azure Databricks and leverage Azure ML for deployment.
+The [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/sdk/python/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 and models with the MLflow instance in Azure Databricks and leverage Azure ML for deployment.
 
 ## Install libraries
 
@@ -242,7 +242,7 @@ mlflow.set_registry_uri(azureml_mlflow_uri)
 > [!NOTE]
 > The value of `azureml_mlflow_uri` was obtained in the same way it was demostrated in [Set MLflow Tracking to only track in your Azure Machine Learning workspace](#tracking-exclusively-on-azure-machine-learning-workspace)
 
-For a complete example about this scenario please check the example [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb).
+For a complete example about this scenario please check the example [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb).
 
 ## Deploying and consuming models registered in Azure Machine Learning
 
@@ -256,7 +256,7 @@ Models registered in Azure Machine Learning Service using MLflow can be consumed
 You can leverage the `azureml-mlflow` plugin to deploy a model to your Azure Machine Learning workspace. Check [How to deploy MLflow models](how-to-deploy-mlflow-models.md) page for a complete detail about how to deploy models to the different targets.
 
 > [!IMPORTANT]
-> Models need to be registered in Azure Machine Learning registry in order to deploy them. If your models happen to be registered in the MLflow instance inside Azure Databricks, you will have to register them again in Azure Machine Learning. If this is you case, please check the example [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/v1/notebooks/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb)
+> Models need to be registered in Azure Machine Learning registry in order to deploy them. If your models happen to be registered in the MLflow instance inside Azure Databricks, you will have to register them again in Azure Machine Learning. If this is you case, please check the example [Training models in Azure Databricks and deploying them on Azure ML](https://github.com/Azure/azureml-examples/blob/main/sdk/python/using-mlflow/no-code-deployment/track_with_databricks_deploy_aml.ipynb)
 
 ### Deploy models to ADB for batch scoring using UDFs
 

articles/machine-learning/how-to-use-mlflow-cli-runs.md

@@ -294,7 +294,7 @@ To register and view a model from a job, use the following steps:
 
 ## Example files
 
-[Using MLflow (Jupyter Notebooks)](https://github.com/Azure/azureml-examples/tree/main/v1/notebooks/using-mlflow)
+[Using MLflow (Jupyter Notebooks)](https://github.com/Azure/azureml-examples/tree/main/sdk/python/using-mlflow)
 
 ## Limitations