Apply suggestions from code review

Co-authored-by: Benjamin Ironside Goldstein <[email protected]>

Author: mdbirnstiehl

solutions/observability/logs/categorize-log-entries.md

@@ -25,7 +25,7 @@ Create a {{ml}} job to categorize log messages automatically. {{ml-cap}} observe
 1. Open the **Categories** page by finding `Logs / Categories` in the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md). You are prompted to use {{ml}} to create log rate categorizations.
 2. Choose a time range for the {{ml}} analysis. By default, the {{ml}} job analyzes log messages no older than four weeks and continues indefinitely.
 3. Add the indices that contain the logs you want to examine. By default, Machine Learning analyzes messages in all log indices that match the patterns set in the **logs sources** advanced setting. To open **Advanced settings**, find **Stack Management** in the main menu or use the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md).
-4. Click **Create ML job**. The job is created, and it starts to run. It takes a few minutes for the {{ml}} robots to collect the necessary data. After the job has processed the data, you can view the results.
+4. Click **Create ML job**. The creates and runs the job. It takes a few minutes for the {{ml}} robots to collect the necessary data. After the job has processed the data, you can view its results.
 
 
 ## Analyze log categories [analyze-log-categories]
@@ -45,7 +45,7 @@ The category row contains the following information:
 * datasets: the name of the datasets where the categories are present.
 * maximum anomaly score: the highest anomaly score in the category.
 
-To view a log message under a particular category, click the arrow at the end of the row. To further examine a message, it can be viewed in **Discover** or displayed in its context.
+To view a log message for a particular category, click the arrow at the end of the row. To further examine it, click **View in Discover** or **View in context**
 
 :::{image} /solutions/images/observability-log-opened.png
 :alt: Opened log category

solutions/observability/logs/inspect-log-anomalies.md

@@ -30,7 +30,7 @@ Create a {{ml}} job to detect anomalous log entry rates automatically.
 1. From the main menu, go to **Other tools** → **Logs Anomalies**, or find `Logs anomalies` in the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md). From here, you’ll be prompted to create a {{ml}} job which will carry out the log rate analysis.
 2. Choose a time range for the {{ml}} analysis.
 3. Add the indices that contain the logs you want to examine. By default, Machine Learning analyzes messages in all log indices that match the patterns set in the **logs source** advanced setting. To open **Advanced settings**, find **Stack Management** in the main menu or use the [global search field](/explore-analyze/find-and-organize/find-apps-and-objects.md).
-4. Click **Create ML job**. The job is created, and it starts to run. It takes a few minutes for the {{ml}} robots to collect the necessary data. After the job has processed the data, you can view the results.
+4. Click **Create ML job**. This creates and runs the job. It takes a few minutes for the {{ml}} robots to collect the necessary data. After the job has processed the data, you can view its results.
 
 ## Anomalies chart [anomalies-chart]