Merge pull request #208379 from Blackmist/v1-v2

V1 v2

Author: v-stsavell

articles/machine-learning/.openpublishing.redirection.machine-learning.json

@@ -1055,6 +1055,11 @@
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@@ -1145,6 +1145,16 @@
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@@ -1221,8 +1231,8 @@
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@@ -1400,6 +1415,21 @@
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@@ -1490,11 +1520,6 @@
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@@ -1505,6 +1530,11 @@
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@@ -1525,6 +1555,11 @@
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@@ -2170,6 +2205,11 @@
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@@ -2212,9 +2252,14 @@
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@@ -2300,6 +2345,11 @@
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@@ -2360,19 +2410,34 @@
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+    {
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+        "redirect_document_id": true
+    },
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@@ -2597,9 +2662,14 @@
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articles/machine-learning/concept-data-encryption.md

@@ -142,6 +142,6 @@ Each workspace has an associated system-assigned managed identity that has the s
 
 * [Connect to Azure storage](how-to-access-data.md)
 * [Get data from a datastore](how-to-create-register-datasets.md)
-* [Connect to data](how-to-connect-data-ui.md)
-* [Train with datasets](how-to-train-with-datasets.md)
+* [Connect to data](v1/how-to-connect-data-ui.md)
+* [Train with datasets](v1/how-to-train-with-datasets.md)
 * [Customer-managed keys](concept-customer-managed-keys.md).

articles/machine-learning/concept-differential-privacy.md

@@ -77,6 +77,6 @@ The system library provides the following tools and services for working with ta
 
 Learn more about differential privacy in machine learning: 
 
- - [How to build a differentially private system](how-to-differential-privacy.md) in Azure Machine Learning.
+ - [How to build a differentially private system](v1/how-to-differential-privacy.md) in Azure Machine Learning with SDK v1.
 
  - To learn more about the components of SmartNoise, check out the GitHub repositories for [SmartNoise Core](https://github.com/opendifferentialprivacy/smartnoise-core), [SmartNoise SDK](https://github.com/opendifferentialprivacy/smartnoise-sdk), and [SmartNoise samples](https://github.com/opendifferentialprivacy/smartnoise-samples).

articles/machine-learning/concept-endpoints.md

@@ -208,7 +208,7 @@ You can [override compute resource settings](how-to-use-batch-endpoint.md#config
 
 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](how-to-connect-data-ui.md)
+- 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.
 
 > [!NOTE]

articles/machine-learning/concept-ml-pipelines.md

@@ -46,7 +46,7 @@ Once the teams get familiar with pipelines and want to do more machine learning
 
 Once a team has built a collection of machine learnings pipelines and reusable components, they could start to build the machine learning pipeline from cloning previous pipeline or tie existing reusable component together. At this stage, the team’s overall productivity will be improved significantly.  
 
-Azure Machine Learning offers different methods to build a pipeline. For users who are familiar with DevOps practices, we recommend using [CLI](how-to-create-component-pipelines-cli.md). For data scientists who are familiar with python, we recommend writing pipeline using the [Azure ML SDK](how-to-create-machine-learning-pipelines.md). For users who prefer to use UI, they could use the [designer to build pipeline by using registered components](how-to-create-component-pipelines-ui.md).
+Azure Machine Learning offers different methods to build a pipeline. For users who are familiar with DevOps practices, we recommend using [CLI](how-to-create-component-pipelines-cli.md). For data scientists who are familiar with python, we recommend writing pipeline using the [Azure ML SDK v1](v1/how-to-create-machine-learning-pipelines.md). For users who prefer to use UI, they could use the [designer to build pipeline by using registered components](how-to-create-component-pipelines-ui.md).
 
 <a name="compare"></a>
 ## Which Azure pipeline technology should I use?

articles/machine-learning/concept-model-management-and-deployment.md

@@ -172,7 +172,7 @@ For more information, see [Enable model data collection](v1/how-to-enable-data-c
 
 ## Retrain your model on new data
 
-Often, you'll want to validate your model, update it, or even retrain it from scratch, as you receive new information. Sometimes, receiving new data is an expected part of the domain. Other times, as discussed in [Detect data drift (preview) on datasets](how-to-monitor-datasets.md), model performance can degrade because of:
+Often, you'll want to validate your model, update it, or even retrain it from scratch, as you receive new information. Sometimes, receiving new data is an expected part of the domain. Other times, as discussed in [Detect data drift (preview) on datasets](v1/how-to-monitor-datasets.md), model performance can degrade because of:
 
 - Changes to a particular sensor.
 - Natural data changes such as seasonal effects.
@@ -202,7 +202,7 @@ For more information on using Azure Pipelines with Machine Learning, see:
 * [Machine Learning MLOps](https://aka.ms/mlops) repository
 * [Machine Learning MLOpsPython](https://github.com/Microsoft/MLOpspython) repository
 
-You can also use Azure Data Factory to create a data ingestion pipeline that prepares data for use with training. For more information, see [Data ingestion pipeline](how-to-cicd-data-ingestion.md).
+You can also use Azure Data Factory to create a data ingestion pipeline that prepares data for use with training. For more information, see [Data ingestion pipeline](v1/how-to-cicd-data-ingestion.md).
 
 ## Next steps
 

articles/machine-learning/concept-sourcing-human-data.md

@@ -232,7 +232,7 @@ For more information on how to work with your data:
 - [Secure data access in Azure Machine Learning](concept-data.md)
 - [Data ingestion options for Azure Machine Learning workflows](concept-data-ingestion.md)
 - [Optimize data processing with Azure Machine Learning](concept-optimize-data-processing.md)
-- [Use differential privacy in Azure Machine Learning](how-to-differential-privacy.md)
+- [Use differential privacy with Azure Machine Learning SDK](v1/how-to-differential-privacy.md)
 
 Follow these how-to guides to work with your data after you've collected it:
 

articles/machine-learning/concept-train-machine-learning-model.md

@@ -70,7 +70,7 @@ Define the iterations, hyperparameter settings, featurization, and other setting
 Machine learning pipelines can use the previously mentioned training methods. Pipelines are more about creating a workflow, so they encompass more than just the training of models. In a pipeline, you can train a model using automated machine learning or run configurations.
 
 * [What are ML pipelines in Azure Machine Learning?](concept-ml-pipelines.md)
-* [Create and run machine learning pipelines with Azure Machine Learning SDK](./how-to-create-machine-learning-pipelines.md)
+* [Create and run machine learning pipelines with Azure Machine Learning SDK](v1/how-to-create-machine-learning-pipelines.md)
 * [Tutorial: Use Azure Machine Learning Pipelines for batch scoring](tutorial-pipeline-batch-scoring-classification.md)
 * [Examples: Jupyter Notebook examples for machine learning pipelines](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learning-pipelines)
 * [Examples: Pipeline with automated machine learning](https://aka.ms/pl-automl)
@@ -95,7 +95,7 @@ The Azure training lifecycle consists of:
 1. Saving logs, model files, and other files written to `./outputs` to the storage account associated with the workspace
 1. Scaling down compute, including removing temporary storage 
 
-If you choose to train on your local machine ("configure as local run"), you do not need to use Docker. You may use Docker locally if you choose (see the section [Configure ML pipeline](./how-to-debug-pipelines.md) for an example).
+If you choose to train on your local machine ("configure as local run"), you do not need to use Docker. You may use Docker locally if you choose (see the section [Configure ML pipeline](v1/how-to-debug-pipelines.md) for an example).
 
 ## Azure Machine Learning designer
 

articles/machine-learning/concept-workspace.md

@@ -118,7 +118,7 @@ When you create a new workspace, it automatically creates several Azure resource
 + [Azure Container Registry](https://azure.microsoft.com/services/container-registry/): Registers docker containers that are used for the following components:
     * [Azure Machine Learning environments](concept-environments.md) when training and deploying models
     * [AutoML](concept-automated-ml.md) when deploying
-    * [Data profiling](how-to-connect-data-ui.md#data-profile-and-preview)
+    * [Data profiling](v1/how-to-connect-data-ui.md#data-profile-and-preview)
 
     To minimize costs, ACR is **lazy-loaded** until images are needed.