Merge pull request #199457 from ssalgadodev/runsToJobsPart9

Runs to jobs

Author: GitHubber17

articles/machine-learning/how-to-auto-train-image-models.md

@@ -522,7 +522,7 @@ When you've configured your AutoML Job to the desired settings, you can submit t
 The automated ML training runs generates output model files, evaluation metrics, logs and deployment artifacts like the scoring file and the environment file which can be viewed from the outputs and logs and metrics tab of the child runs.
 
 > [!TIP]
-> Check how to navigate to the run results from the  [View run results](how-to-understand-automated-ml.md#view-run-results) section.
+> Check how to navigate to the run results from the  [View run results](how-to-understand-automated-ml.md#view-job-results) section.
 
 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#metrics-for-image-models-preview)
 

articles/machine-learning/how-to-train-with-ui.md

@@ -37,6 +37,7 @@ There are many ways to create a training job with Azure Machine Learning. You ca
 * Or, you may enter the job creation from the left pane. Click **+New** and select **Job**. 
 [![Azure Machine Learning studio left navigation](media/how-to-train-with-ui/left-nav-entry.png)](media/how-to-train-with-ui/left-nav-entry.png)
 
+
 These options will all take you to the job creation panel, which has a wizard for configuring and creating a training job. 
 
 ## Select compute resources
@@ -77,7 +78,7 @@ After selecting a compute target, you need to specify the runtime environment fo
 
 ### Curated environments
 
-Curated environments are Azure-defined collections of Python packages used in common ML workloads. Curated environments are available in your workspace by default. These environments are backed by cached Docker images, which reduce the run preparation overhead. The cards displayed in the "Curated environments" page show details of each environment. To learn more, see [curated environments in Azure Machine Learning](resource-curated-environments.md).
+Curated environments are Azure-defined collections of Python packages used in common ML workloads. Curated environments are available in your workspace by default. These environments are backed by cached Docker images, which reduce the job preparation overhead. The cards displayed in the "Curated environments" page show details of each environment. To learn more, see [curated environments in Azure Machine Learning](resource-curated-environments.md).
 
  [![Curated environments](media/how-to-train-with-ui/curated-env.png)](media/how-to-train-with-ui/curated-env.png)
 
@@ -139,7 +140,7 @@ You may choose **view the YAML spec** to review and download the yaml file gener
 [![view yaml spec](media/how-to-train-with-ui/view-yaml.png)](media/how-to-train-with-ui/view-yaml.png)
 [![Yaml spec](media/how-to-train-with-ui/yaml-spec.png)](media/how-to-train-with-ui/yaml-spec.png)
 
-To launch the job, choose **Create**. Once the job is created, Azure will show you the run details page, where you can monitor and manage your training job. 
+To launch the job, choose **Create**. Once the job is created, Azure will show you the job details page, where you can monitor and manage your training job. 
 
   [!INCLUDE [Email Notification Include](../../includes/machine-learning-email-notifications.md)]
 

articles/machine-learning/how-to-trigger-published-pipeline.md

@@ -53,7 +53,7 @@ pipeline_id = "aaaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee"
 
 ## Create a schedule
 
-To run a pipeline on a recurring basis, you'll create a schedule. A `Schedule` associates a pipeline, an experiment, and a trigger. The trigger can either be a`ScheduleRecurrence` that describes the wait between runs or a Datastore path that specifies a directory to watch for changes. In either case, you'll need the pipeline identifier and the name of the experiment in which to create the schedule.
+To run a pipeline on a recurring basis, you'll create a schedule. A `Schedule` associates a pipeline, an experiment, and a trigger. The trigger can either be a`ScheduleRecurrence` that describes the wait between jobs or a Datastore path that specifies a directory to watch for changes. In either case, you'll need the pipeline identifier and the name of the experiment in which to create the schedule.
 
 At the top of your Python file, import the `Schedule` and `ScheduleRecurrence` classes:
 
@@ -66,7 +66,7 @@ from azureml.pipeline.core.schedule import ScheduleRecurrence, Schedule
 
 The `ScheduleRecurrence` constructor has a required `frequency` argument that must be one of the following strings: "Minute", "Hour", "Day", "Week", or "Month". It also requires an integer `interval` argument specifying how many of the `frequency` units should elapse between schedule starts. Optional arguments allow you to be more specific about starting times, as detailed in the [ScheduleRecurrence SDK docs](/python/api/azureml-pipeline-core/azureml.pipeline.core.schedule.schedulerecurrence).
 
-Create a `Schedule` that begins a run every 15 minutes:
+Create a `Schedule` that begins a job every 15 minutes:
 
 ```python
 recurrence = ScheduleRecurrence(frequency="Minute", interval=15)
@@ -79,7 +79,7 @@ recurring_schedule = Schedule.create(ws, name="MyRecurringSchedule",
 
 ### Create a change-based schedule
 
-Pipelines that are triggered by file changes may be more efficient than time-based schedules. When you want to do something before a file is changed, or when a new file is added to a data directory, you can preprocess that file. You can monitor any changes to a datastore or changes within a specific directory within the datastore. If you monitor a specific directory, changes within subdirectories of that directory will _not_ trigger a run.
+Pipelines that are triggered by file changes may be more efficient than time-based schedules. When you want to do something before a file is changed, or when a new file is added to a data directory, you can preprocess that file. You can monitor any changes to a datastore or changes within a specific directory within the datastore. If you monitor a specific directory, changes within subdirectories of that directory will _not_ trigger a job.
 
 To create a file-reactive `Schedule`, you must set the `datastore` parameter in the call to [Schedule.create](/python/api/azureml-pipeline-core/azureml.pipeline.core.schedule.schedule#create-workspace--name--pipeline-id--experiment-name--recurrence-none--description-none--pipeline-parameters-none--wait-for-provisioning-false--wait-timeout-3600--datastore-none--polling-interval-5--data-path-parameter-name-none--continue-on-step-failure-none--path-on-datastore-none---workflow-provider-none---service-endpoint-none-). To monitor a folder, set the `path_on_datastore` argument.
 
@@ -104,7 +104,7 @@ In your Web browser, navigate to Azure Machine Learning. From the **Endpoints**
 
 :::image type="content" source="./media/how-to-trigger-published-pipeline/scheduled-pipelines.png" alt-text="Pipelines page of AML":::
 
-In this page you can see summary information about all the pipelines in the Workspace: names, descriptions, status, and so forth. Drill in by clicking in your pipeline. On the resulting page, there are more details about your pipeline and you may drill down into individual runs.
+In this page you can see summary information about all the pipelines in the Workspace: names, descriptions, status, and so forth. Drill in by clicking in your pipeline. On the resulting page, there are more details about your pipeline and you may drill down into individual jobs.
 
 ## Deactivate the pipeline
 
@@ -221,7 +221,7 @@ In an Azure Data Factory pipeline, the *Machine Learning Execute Pipeline* activ
 
 ## Next steps
 
-In this article, you used the Azure Machine Learning SDK for Python to schedule a pipeline in two different ways. One schedule recurs based on elapsed clock time. The other schedule runs if a file is modified on a specified `Datastore` or within a directory on that store. You saw how to use the portal to examine the pipeline and individual runs. You learned how to disable a schedule so that the pipeline stops running. Finally, you created an Azure Logic App to trigger a pipeline. 
+In this article, you used the Azure Machine Learning SDK for Python to schedule a pipeline in two different ways. One schedule recurs based on elapsed clock time. The other schedule jobs if a file is modified on a specified `Datastore` or within a directory on that store. You saw how to use the portal to examine the pipeline and individual jobs. You learned how to disable a schedule so that the pipeline stops running. Finally, you created an Azure Logic App to trigger a pipeline. 
 
 For more information, see:
 

articles/machine-learning/how-to-troubleshoot-auto-ml.md

@@ -148,14 +148,14 @@ If the listed version is not a supported version:
 
 ## Data access
  
-For automated ML runs, you need to ensure the file datastore that connects to your AzureFile storage has the appropriate authentication credentials. Otherwise, the following message results. Learn how to [update your data access authentication credentials](how-to-train-with-datasets.md#azurefile-storage).
+For automated ML jobs, you need to ensure the file datastore that connects to your AzureFile storage has the appropriate authentication credentials. Otherwise, the following message results. Learn how to [update your data access authentication credentials](how-to-train-with-datasets.md#azurefile-storage).
 
 Error message: 
 `Could not create a connection to the AzureFileService due to missing credentials. Either an Account Key or SAS token needs to be linked the default workspace blob store.`
 
 ## Data schema
 
-When you try to create a new automated ML experiment via the **Edit and submit** button in the Azure Machine Learning studio, the data schema for the new experiment must match the schema of the data that was used in the original experiment. Otherwise, an error message similar to the following results. Learn more about how to [edit and submit experiments from the studio UI](how-to-use-automated-ml-for-ml-models.md#edit-and-submit-runs-preview).
+When you try to create a new automated ML experiment via the **Edit and submit** button in the Azure Machine Learning studio, the data schema for the new experiment must match the schema of the data that was used in the original experiment. Otherwise, an error message similar to the following results. Learn more about how to [edit and submit experiments from the studio UI](how-to-use-automated-ml-for-ml-models.md#edit-and-submit-jobs-preview).
 
 Error message non-vision experiments: ` Schema mismatch error: (an) additional column(s): "Column1: String, Column2: String, Column3: String", (a) missing column(s)`
 

articles/machine-learning/how-to-troubleshoot-batch-endpoints.md

@@ -65,7 +65,7 @@ Because of the distributed nature of batch scoring jobs, there are logs from sev
 
 - `~/logs/job_progress_overview.txt`: This file provides high-level information about the number of mini-batches (also known as tasks) created so far and the number of mini-batches processed so far. As the mini-batches end, the log records the results of the job. If the job failed, it will show the error message and where to start the troubleshooting.
 
-- `~/logs/sys/master_role.txt`: This file provides the principal node (also known as the orchestrator) view of the running job. This log provides information on task creation, progress monitoring, the run result.
+- `~/logs/sys/master_role.txt`: This file provides the principal node (also known as the orchestrator) view of the running job. This log provides information on task creation, progress monitoring, the job result.
 
 For a concise understanding of errors in your script there is:
 

articles/machine-learning/how-to-troubleshoot-environments.md

@@ -26,7 +26,7 @@ Learn how to troubleshoot issues with Docker environment image builds and packag
 ## Docker image build failures
 
 For most image build failures, you'll find the root cause in the image build log.
-Find the image build log from the Azure Machine Learning portal (20\_image\_build\_log.txt) or from your Azure Container Registry task run logs.
+Find the image build log from the Azure Machine Learning portal (20\_image\_build\_log.txt) or from your Azure Container Registry task job logs.
 
 It's usually easier to reproduce errors locally. Check the kind of error and try one of the following `setuptools`:
 

articles/machine-learning/how-to-understand-automated-ml.md

@@ -1,7 +1,7 @@
 ---
 title: Evaluate AutoML experiment results
 titleSuffix: Azure Machine Learning
-description: Learn how to view and evaluate charts and metrics for each of your automated machine learning experiment runs. 
+description: Learn how to view and evaluate charts and metrics for each of your automated machine learning experiment jobs. 
 services: machine-learning
 ms.author: larryfr
 author: blackmist
@@ -14,7 +14,7 @@ ms.custom: contperf-fy21q2, automl, event-tier1-build-2022
 
 # Evaluate automated machine learning experiment results
 
-In this article, learn how to evaluate and compare models trained by your automated machine learning (automated ML) experiment. Over the course of an automated ML experiment, many runs are created and each run creates a model. For each model, automated ML generates evaluation metrics and charts that help you measure the model's performance. 
+In this article, learn how to evaluate and compare models trained by your automated machine learning (automated ML) experiment. Over the course of an automated ML experiment, many jobs are created and each job creates a model. For each model, automated ML generates evaluation metrics and charts that help you measure the model's performance. 
 
 For example, automated ML generates the following charts based on experiment type.
 
@@ -35,18 +35,18 @@ For example, automated ML generates the following charts based on experiment typ
   - The [Azure Machine Learning studio](how-to-use-automated-ml-for-ml-models.md) (no code required)
   - The [Azure Machine Learning Python SDK](how-to-configure-auto-train.md)
 
-## View run results
+## View job results
 
-After your automated ML experiment completes, a history of the runs can be found via:
+After your automated ML experiment completes, a history of the jobs can be found via:
   - A browser with [Azure Machine Learning studio](overview-what-is-machine-learning-studio.md)
-  - A Jupyter notebook using the [RunDetails Jupyter widget](/python/api/azureml-widgets/azureml.widgets.rundetails)
+  - A Jupyter notebook using the [JobDetails Jupyter widget](/python/api/azureml-widgets/azureml.widgets.rundetails)
 
 The following steps and video, show you how to view the run history and model evaluation metrics and charts in the studio:
 
 1. [Sign into the studio](https://ml.azure.com/) and navigate to your workspace.
 1. In the left menu, select **Experiments**.
 1. Select your experiment from the list of experiments.
-1. In the table at the bottom of the page, select an automated ML run.
+1. In the table at the bottom of the page, select an automated ML job.
 1. In the **Models** tab, select the **Algorithm name** for the model you want to evaluate.
 1. In the **Metrics** tab, use the checkboxes on the left to view metrics and charts.