Updates from PM review

Author: johnwu0604

articles/machine-learning/how-to-log-pipelines-application-insights.md

@@ -12,13 +12,11 @@ ms.topic: how-to
 ms.custom: devx-track-python, sdkv1, event-tier1-build-2022
 ---
 
-[//]: # (needs PM review; pipeline jobs? Or pipeline runs? lots of code, please change the code as needed )
-
 # Collect machine learning pipeline log files in Application Insights for alerts and debugging
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
 
-The [OpenCensus](https://opencensus.io/quickstart/python/) Python library can be used to route logs to Application Insights from your scripts. Aggregating logs from pipeline jobs in one place allows you to build queries and diagnose issues. Using Application Insights will allow you to track logs over time and compare pipeline logs across jobs.
+The [OpenCensus](https://opencensus.io/quickstart/python/) Python library can be used to route logs to Application Insights from your scripts. Aggregating logs from pipeline runs in one place allows you to build queries and diagnose issues. Using Application Insights will allow you to track logs over time and compare pipeline logs across runs.
 
 Having your logs in once place will provide a history of exceptions and error messages. Since Application Insights integrates with Azure Alerts, you can also create alerts based on Application Insights queries.
 
@@ -64,7 +62,7 @@ sample_step = PythonScriptStep(
         runconfig=run_config
 )
 
-# Submit new pipeline job
+# Submit new pipeline run
 pipeline = Pipeline(workspace=ws, steps=[sample_step])
 pipeline.submit(experiment_name="Logging_Experiment")
 ```
@@ -90,9 +88,9 @@ logger.warning("I will be sent to Application Insights")
 
 ## Logging with Custom Dimensions
 
-By default, logs forwarded to Application Insights won't have enough context to trace back to the job or experiment. To make the logs actionable for diagnosing issues, additional fields are needed. 
+By default, logs forwarded to Application Insights won't have enough context to trace back to the run or experiment. To make the logs actionable for diagnosing issues, additional fields are needed. 
 
-To add these fields, Custom Dimensions can be added to provide context to a log message. One example is when someone wants to view logs across multiple steps in the same pipeline job.
+To add these fields, Custom Dimensions can be added to provide context to a log message. One example is when someone wants to view logs across multiple steps in the same pipeline run.
 
 Custom Dimensions make up a dictionary of key-value (stored as string, string) pairs. The dictionary is then sent to Application Insights and displayed as a column in the query results. Its individual dimensions can be used as [query parameters](#additional-helpful-queries).
 

articles/machine-learning/how-to-log-view-metrics.md

@@ -13,8 +13,6 @@ ms.topic: how-to
 ms.custom: sdkv1, event-tier1-build-2022
 ---
 
-[//]: # (needs PM review; Lots of code, what needs to be changed?)
-
 # Log & view metrics and log files
 
 > [!div class="op_single_selector" title1="Select the version of Azure Machine Learning Python SDK you are using:"]
@@ -165,7 +163,7 @@ params = finished_mlflow_run.data.params
 
 You can browse completed job records, including logged metrics, in the [Azure Machine Learning studio](https://ml.azure.com).
 
-Navigate to the **Experiments** tab. To view all your jobs in your Workspace across Experiments, select the **All jobs** tab. You can drill down on jobs for specific Experiments by applying the Experiment filter in the top menu bar.
+Navigate to the **Jobs** tab. To view all your jobs in your Workspace across Experiments, select the **All jobs** tab. You can drill down on jobs for specific Experiments by applying the Experiment filter in the top menu bar.
 
 For the individual Experiment view, select the **All experiments** tab. On the experiment run dashboard, you can see tracked metrics and logs for each run. 
 
@@ -176,7 +174,7 @@ You can also edit the job list table to select multiple jobs and display either
 
 Log files are an essential resource for debugging the Azure ML workloads. After submitting a training job, drill down to a specific run to view its logs and outputs:  
 
-1. Navigate to the **Experiments** tab.
+1. Navigate to the **Jobs** tab.
 1. Select the runID for a specific run.
 1. Select **Outputs and logs** at the top of the page.
 2. Select **Download all** to download all your logs into a zip folder.

articles/machine-learning/how-to-machine-learning-fairness-aml.md

@@ -13,8 +13,6 @@ ms.topic: how-to
 ms.custom: devx-track-python, responsible-ml, sdkv1, event-tier1-build-2022
 ---
 
-[//]: # (needs PM review; what happens with the code?)
-
 # Use Azure Machine Learning with the Fairlearn open-source package to assess the fairness of ML models (preview)
 
 [!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
@@ -183,7 +181,7 @@ The following example shows how to use the fairness package. We will upload mode
     ```python
     from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id
     ```
-    Create an Experiment, then a Job, and upload the dashboard to it:
+    Create an Experiment, then a Run, and upload the dashboard to it:
     ```python
     exp = Experiment(ws, "Test_Fairness_Census_Demo")
     print(exp)
@@ -209,10 +207,10 @@ The following example shows how to use the fairness package. We will upload mode
     If you complete the previous steps (uploading generated fairness insights to Azure Machine Learning), you can view the fairness dashboard in [Azure Machine Learning studio](https://ml.azure.com). This dashboard is the same visualization dashboard provided in Fairlearn, enabling you to analyze the disparities among your sensitive feature's subgroups (e.g., male vs. female).
     Follow one of these paths to access the visualization dashboard in Azure Machine Learning studio:
 
-    * **Experiments pane (Preview)**
-    1. Select **Experiments** in the left pane to see a list of experiments that you've run on Azure Machine Learning.
-    1. Select a particular experiment to view all the jobs in that experiment.
-    1. Select a job, and then the **Fairness** tab to the explanation visualization dashboard.
+    * **Jobs pane (Preview)**
+    1. Select **Jobs** in the left pane to see a list of experiments that you've run on Azure Machine Learning.
+    1. Select a particular experiment to view all the runs in that experiment.
+    1. Select a run, and then the **Fairness** tab to the explanation visualization dashboard.
     1. Once landing on the **Fairness** tab, click on a **fairness id** from the menu on the right.
     1. Configure your dashboard by selecting your sensitive attribute, performance metric, and fairness metric of interest to land on the fairness assessment page.
     1. Switch chart type from one to another to observe both **allocation** harms and **quality of service** harms.

articles/machine-learning/how-to-manage-environments-in-studio.md

@@ -12,8 +12,6 @@ ms.topic: how-to
 ms.custom: devx-track-python
 ---
 
-[//]: # (needs PM review)
-
 # Manage software environments in Azure Machine Learning studio
 
 In this article, learn how to create and manage Azure Machine Learning [environments](/python/api/azureml-core/azureml.core.environment.environment) in the Azure Machine Learning studio. Use the environments to track and reproduce your projects' software dependencies as they evolve.