Updates ML Anomaly Detection tutorial

explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md

@@ -101,7 +101,7 @@ For more information, see [{{dfeeds-cap}}](ml-ad-run-jobs.md#ml-ad-datafeeds), [
 
 ::::
 
-If you want to see all of the configuration details for your jobs and {{dfeeds}}, you can do so on the **Machine Learning** > **Anomaly Detection** > **Jobs** page. Alternatively, you can see the configuration files in [GitHub ](https://github.com/elastic/kibana/tree/master/x-pack/platform/plugins/shared/ml/server/models/data_recognizer/modules/sample_data_weblogs). For the purposes of this tutorial, however, here’s a quick overview of the goal of each job:
+If you want to see all of the configuration details for your jobs and {{dfeeds}}, you can do so on the **Stack Management** > **Anomaly Detection Jobs** page. Alternatively, you can see the configuration files in [GitHub ](https://github.com/elastic/kibana/tree/master/x-pack/platform/plugins/shared/ml/server/models/data_recognizer/modules/sample_data_weblogs). For the purposes of this tutorial, however, here’s a quick overview of the goal of each job:
 
 * `low_request_rate` uses the `low_count` function to find unusually low request rates
 * `response_code_rates` uses the `count` function and partitions the analysis by `response.keyword` values to find unusual event rates by HTTP response code
@@ -122,7 +122,7 @@ Depending on the capacity of your machine, you might need to wait a few seconds
 :screenshot:
 :::
 
-The {{ml-features}} analyze the input stream of data, model its behavior, and perform analysis based on the detectors in each job. When an event occurs outside of the model, that event is identified as an anomaly. You can immediately see that all three jobs have found anomalies, which are indicated by red blocks in the swim lanes for each job.
+The {{ml-features}} analyze the input stream of data, model its behavior, and perform analysis based on the detectors in each job. When an event occurs outside of the model, that event is identified as an anomaly. You can immediately see that all three jobs have found anomalies, which are indicated by red blocks in the swim lanes for each job. Swim-lane colors map to the anomaly score: blue indicates low, yellow indicates medium, and red indicates high severity, with different shades representing varying levels within each category.
 
 There are two tools for examining the results from {{anomaly-jobs}} in {{kib}}: the **Anomaly Explorer** and the **Single Metric Viewer**. You can switch between these tools by clicking the icons in the top left corner. You can also edit the job selection to examine a different subset of {{anomaly-jobs}}.
 
@@ -266,7 +266,7 @@ In addition to detecting anomalous behavior in your data, you can use the {{ml-f
 
 To create a forecast in {{kib}}:
 
-1. View your job results (for example, for the `low_request_rate` job) in the **Single Metric Viewer**. To find that view, click the **View series** button in the **Actions** column on the **Anomaly Detection** page.
+1. View your job results (for example, for the `low_request_rate` job) in the **Single Metric Viewer**. To find that view, navigate to **Machine Learning** > **Single Metric Viewer** and select the job.
 2. Click **Forecast**.
    :::{image} /explore-analyze/images/machine-learning-ml-gs-forecast.png
    :alt: Create a forecast from the Single Metric Viewer