rearrange articles, from Shane

Author: sdgilley

.openpublishing.redirection.json

@@ -26530,6 +26530,21 @@
       "redirect_url": "/azure/machine-learning/classic/export-delete-personal-data-dsr",
       "redirect_document_id": true
     },
+    {
+      "source_path_from_root": "/articles/machine-learning/how-to-monitor-view-training-logs.md",
+      "redirect_url": "/azure/machine-learning/how-to-log-and-view.metrics",
+      "redirect_document_id": false
+    },
+    {
+      "source_path_from_root": "/articles/machine-learning/how-to-manage-runs.md",
+      "redirect_url": "/azure/machine-learning/how-to-track-monitor-analyze-runs",
+      "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/machine-learning/how-to-track-experiments.md",
+      "redirect_url": "/azure/machine-learning/how-to-log-and-view-metrics",
+      "redirect_document_id": true
+    },
     {
       "source_path_from_root": "/articles/machine-learning/machine-learning-faq.md",
       "redirect_url": "/azure/machine-learning/classic/faq",

articles/machine-learning/concept-azure-machine-learning-architecture.md

@@ -113,9 +113,9 @@ When you submit a run, Azure Machine Learning compresses the directory that cont
 
 ### Logging
 
-Azure Machine Learning automatically logs standard run metrics for you. However, you can also [use the Python SDK to log arbitrary metrics](how-to-track-experiments.md).
+Azure Machine Learning automatically logs standard run metrics for you. However, you can also [use the Python SDK to log arbitrary metrics](how-to-log-and-view-metrics.md).
 
-There are multiple ways to view your logs: monitoring run status in real time, or viewing results after completion. For more information, see [Monitor and view ML run logs](how-to-monitor-view-training-logs.md).
+There are multiple ways to view your logs: monitoring run status in real time, or viewing results after completion. For more information, see [Monitor and view ML run logs](how-to-log-and-view-metrics.md).
 
 
 > [!NOTE]
@@ -253,8 +253,8 @@ Pipeline steps are reusable, and can be run without rerunning the previous steps
 Azure Machine Learning provides the following monitoring and logging capabilities:
 
 * For __Data Scientists__, you can monitor your experiments and log information from your training runs. For more information, see the following articles:
-   * [Start, monitor, and cancel training runs](how-to-manage-runs.md)
-   * [Log metrics for training runs](how-to-track-experiments.md)
+   * [Start, monitor, and cancel training runs](how-to-track-monitor-analyze-runs.md)
+   * [Log metrics for training runs](how-to-log-and-view-metrics.md)
    * [Track experiments with MLflow](how-to-use-mlflow.md)
    * [Visualize runs with TensorBoard](how-to-monitor-tensorboard.md)
 * For __Administrators__, you can monitor information about the workspace, related Azure resources, and events such as resource creation and deletion by using Azure Monitor. For more information, see [How to monitor Azure Machine Learning](monitor-azure-machine-learning.md).

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

@@ -42,7 +42,7 @@ After the pipeline is designed, there is often more fine-tuning around the train
 
 With pipelines, you may choose to use different hardware for different tasks. Azure coordinates the various [compute targets](concept-azure-machine-learning-architecture.md) you use, so your intermediate data seamlessly flows to downstream compute targets.
 
-You can [track the metrics for your pipeline experiments](./how-to-track-experiments.md) directly in  Azure portal or your [workspace landing page (preview)](https://ml.azure.com). After a pipeline has been published, you can configure a REST endpoint, which allows you to rerun the pipeline from any platform or stack.
+You can [track the metrics for your pipeline experiments](./how-to-log-and-view-metrics.md) directly in  Azure portal or your [workspace landing page (preview)](https://ml.azure.com). After a pipeline has been published, you can configure a REST endpoint, which allows you to rerun the pipeline from any platform or stack.
 
 In short, all of the complex tasks of the machine learning lifecycle can be helped with pipelines. Other Azure pipeline technologies have their own strengths. [Azure Data Factory pipelines](../data-factory/concepts-pipelines-activities.md) excels at working with data and [Azure Pipelines](https://azure.microsoft.com/services/devops/pipelines/) is the right tool for continuous integration and deployment. But if your focus is machine learning, Azure Machine Learning pipelines are likely to be the best choice for your workflow needs. 
 

articles/machine-learning/data-science-virtual-machine/how-to-track-experiments.md

@@ -390,7 +390,7 @@ Configure  `max_concurrent_iterations` in your `AutoMLConfig` object. If it is n
 
 Automated ML offers options for you to monitor and evaluate your training results. 
 
-* You can view your training results in a widget or inline if you are in a notebook. See [how to monitor automated ML runs](how-to-monitor-view-training-logs.md#monitor-automated-machine-learning-runs) for more details.
+* You can view your training results in a widget or inline if you are in a notebook. See [Monitor automated machine learning runs](#monitor) for more details.
 
 * For definitions and examples of the performance charts and metrics provided for each run, see [Evaluate automated machine learning experiment results](how-to-understand-automated-ml.md) . 
 
@@ -493,6 +493,22 @@ print_model(model_from_aml)
 > [!NOTE]
 > The algorithms automated ML employs have inherent randomness that can cause slight variation in a recommended model's final metrics score, like accuracy. Automated ML also performs operations on data such as train-test split, train-validation split or cross-validation when necessary. So if you run an experiment with the same configuration settings and primary metric multiple times, you'll likely see variation in each experiments final metrics score due to these factors. 
 
+## <a name="monitor"></a> Monitor automated machine learning runs
+
+For automated machine learning runs, to access the charts from a previous run, replace `<<experiment_name>>` with the appropriate experiment name:
+
+```python
+from azureml.widgets import RunDetails
+from azureml.core.run import Run
+
+experiment = Experiment (workspace, <<experiment_name>>)
+run_id = 'autoML_my_runID' #replace with run_ID
+run = Run(experiment, run_id)
+RunDetails(run).show()
+```
+
+![Jupyter notebook widget for Automated Machine Learning](./media/how-to-configure-auto-train/azure-machine-learning-auto-ml-widget.png)
+
 ## Register and deploy models
 
 You can register a model, so you can come back to it for later use. 

articles/machine-learning/how-to-configure-auto-train.md

@@ -189,8 +189,8 @@ The table below provides information for different debug options for pipelines.
 
 | Library                    | Type   | Example                                                          | Destination                                  | Resources                                                                                                                                                                                                                                                                                                                    |
 |----------------------------|--------|------------------------------------------------------------------|----------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| Azure Machine Learning SDK | Metric | `run.log(name, val)`                                             | Azure Machine Learning Portal UI             | [How to track experiments](how-to-track-experiments.md)<br>[azureml.core.Run class](/python/api/azureml-core/azureml.core.run%28class%29)                                                                                                                                                 |
-| Python printing/logging    | Log    | `print(val)`<br>`logging.info(message)`                          | Driver logs, Azure Machine Learning designer | [How to track experiments](how-to-track-experiments.md)<br><br>[Python logging](https://docs.python.org/2/library/logging.html)                                                                                                                                                                       |
+| Azure Machine Learning SDK | Metric | `run.log(name, val)`                                             | Azure Machine Learning Portal UI             | [How to track experiments](how-to-log-and-view-metrics.md)<br>[azureml.core.Run class](/python/api/azureml-core/azureml.core.run%28class%29)                                                                                                                                                 |
+| Python printing/logging    | Log    | `print(val)`<br>`logging.info(message)`                          | Driver logs, Azure Machine Learning designer | [How to track experiments](how-to-log-and-view-metrics.md)<br><br>[Python logging](https://docs.python.org/2/library/logging.html)                                                                                                                                                                       |
 | OpenCensus Python          | Log    | `logger.addHandler(AzureLogHandler())`<br>`logging.log(message)` | Application Insights - traces                | [Debug pipelines in Application Insights](./how-to-log-pipelines-application-insights.md)<br><br>[OpenCensus Azure Monitor Exporters](https://github.com/census-instrumentation/opencensus-python/tree/master/contrib/opencensus-ext-azure)<br>[Python logging cookbook](https://docs.python.org/3/howto/logging-cookbook.html) |
 
 #### Logging options example

articles/machine-learning/how-to-debug-pipelines.md

@@ -274,7 +274,7 @@ ip_address: 10.3.0.5
 Save the `ip_address` value. It is used in the next section.
 
 > [!TIP]
-> You can also find the IP address from the run logs for the child run for this pipeline step. For more information on viewing this information, see [Monitor Azure ML experiment runs and metrics](how-to-track-experiments.md).
+> You can also find the IP address from the run logs for the child run for this pipeline step. For more information on viewing this information, see [Monitor Azure ML experiment runs and metrics](how-to-log-and-view-metrics.md).
 
 ### Configure development environment