Merge branch 'master' of https://github.com/MicrosoftDocs/azure-docs-pr into update-faq

Author: eross-msft

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

@@ -124,7 +124,7 @@ The [`AutoMLConfig`](https://docs.microsoft.com/python/api/azureml-train-automl-
 
 See the [reference documentation](/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig) for more information.
 
-Create the time-series settings as a dictionary object. Set the `time_column_name` to the `day_datetime` field in the data set. Define the `grain_column_names` parameter to ensure that **two separate time-series groups** are created for the data; one for store A and B. Lastly, set the `max_horizon` to 50 in order to predict for the entire test set. Set a forecast window to 10 periods with `target_rolling_window_size`, and specify a single lag on the target values for two periods ahead with the `target_lags` parameter.
+Create the time-series settings as a dictionary object. Set the `time_column_name` to the `day_datetime` field in the data set. Define the `grain_column_names` parameter to ensure that **two separate time-series groups** are created for the data; one for store A and B. Lastly, set the `max_horizon` to 50 in order to predict for the entire test set. Set a forecast window to 10 periods with `target_rolling_window_size`, and specify a single lag on the target values for two periods ahead with the `target_lags` parameter. It is recommended to set `max_horizon`, `target_rolling_window_size` and `target_lags` to "auto" which will automatically detect these values for you. In the example below, "auto" settings have been used for these paramaters. 
 
 ```python
 time_series_settings = {

articles/machine-learning/how-to-manage-runs.md

@@ -10,14 +10,13 @@ ms.author: roastala
 author: rastala
 manager: cgronlun
 ms.reviewer: nibaccam
-ms.date: 11/04/2019
-
+ms.date: 01/09/2020
 ---
 
 # Start, monitor, and cancel training runs in Python
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
-The [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py) and [Machine Learning CLI](reference-azure-machine-learning-cli.md) provide various methods to monitor, organize, and manage your runs for training and experimentation.
+The [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py), [Machine Learning CLI](reference-azure-machine-learning-cli.md), and [Azure Machine Learning studio](https://ml.azure.com) provide various methods to monitor, organize, and manage your runs for training and experimentation.
 
 This article shows examples of the following tasks:
 
@@ -101,6 +100,16 @@ To start a run of your experiment, use the following steps:
 
     For more information, see [az ml run submit-script](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/run?view=azure-cli-latest#ext-azure-cli-ml-az-ml-run-submit-script).
 
+### Using Azure Machine Learning studio
+
+To start a submit a pipeline run in the designer (preview), use the following steps:
+
+1. Set a default compute target for your pipeline.
+
+1. Select **Run** at the top of the pipeline canvas.
+
+1. Select an Experiment to group your pipeline runs.
+
 ## Monitor the status of a run
 
 ### Using the SDK
@@ -156,6 +165,22 @@ print(notebook_run.get_status())
 
     For more information, see [az ml run show](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/run?view=azure-cli-latest#ext-azure-cli-ml-az-ml-run-show).
 
+
+### Using Azure Machine Learning studio
+
+To view the number of active runs for your experiment in the studio.
+
+1. Navigate to the **Experiments** section.. 
+
+1. Select an experiment.
+
+    In the experiment page, you can see the number of active compute targets and the duration for each run. 
+
+1. Select a specific run number.
+
+1. In the **Logs** tab, you can find diagnostic and error logs for your pipeline run.
+
+
 ## Cancel or fail runs
 
 If you notice a mistake or if your run is taking too long to finish, you can cancel the run.
@@ -191,6 +216,17 @@ az ml run cancel -r runid -w workspace_name -e experiment_name
 
 For more information, see [az ml run cancel](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/run?view=azure-cli-latest#ext-azure-cli-ml-az-ml-run-cancel).
 
+### Using Azure Machine Learning studio
+
+To cancel a run in the studio, using the following steps:
+
+1. Go to the running pipeline in either the **Experiments** or **Pipelines** section. 
+
+1. Select the pipeline run number you want to cancel.
+
+1. In the toolbar, select **Cancel**
+
+
 ## Create child runs
 
 Create child runs to group together related runs, such as for different hyperparameter-tuning iterations.
@@ -333,6 +369,12 @@ az ml run list --experiment-name experiment [?properties.author=='azureml-user'
 
 For more information on querying Azure CLI results, see [Query Azure CLI command output](https://docs.microsoft.com/cli/azure/query-azure-cli?view=azure-cli-latest).
 
+### Using Azure Machine Learning studio
+
+1. Navigate to the **Pipelines** section.
+
+1. Use the search bar to filter pipelines using tags, descriptions, experiment names, and submitter name.
+
 ## Example notebooks
 
 The following notebooks demonstrate the concepts in this article:

articles/media-services/video-indexer/release-notes.md

@@ -11,7 +11,7 @@ ms.service: media-services
 ms.subservice: video-indexer
 ms.workload: na
 ms.topic: article
-ms.date: 01/07/2019
+ms.date: 01/07/2020
 ms.author: juliako
 ---
 
@@ -34,7 +34,13 @@ Update a specific section in the transcript using the [Update-Video-Index](https
 
 ### Fix account configuration from the Video Indexer portal
 
-You can now update Media Services connection configuration in order to self-help with issues like: incorrect Azure Media Services resource. To fix the account configuration, in Video Indexer portal navigate to Settings > Account tab (as owner).
+You can now update Media Services connection configuration in order to self-help with issues like: 
+
+* incorrect Azure Media Services resource
+* password was changed
+* Media Services resources were moved between subscriptions  
+
+To fix the account configuration, in Video Indexer portal navigate to Settings > Account tab (as owner).
 
 ### Configure the custom vision account