diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md b/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md
index e3ca80bc40..f9e16e916b 100644
--- a/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md
+++ b/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md
@@ -84,7 +84,7 @@ The results on this page might be different than the actual values you get when
 
 The {{kib}} sample data sets include some pre-configured {{anomaly-jobs}} for you to play with. You can use either of the following methods to add the jobs:
 
-* After you load the sample web logs data set on the {{kib}} home page, click **View data** > **ML jobs**.
+* Load the sample web logs data set, then click **View data** > **ML jobs**. To access the sample date in {{ech}}, go to the {{kib}} home page, click **Other sample data sets**, then go to the sample web logs card in the expanded section and click **Add data**. To access the sample data in {{serverless-short}}, go to the **Integrations** page in the navigation menu or using the [global search bar](/explore-analyze/find-and-organize/find-apps-and-objects.md), and search for "Sample Data".
 * In the Machine Learning app, when you select the `kibana_sample_data_logs` {{data-sources}} in the **Data Visualizer** or the **Anomaly Detection** job wizards, it recommends that you create a job using its known configuration. Select the **Kibana sample data web logs** configuration.
 * Accept the default values and click **Create Jobs**.
 
@@ -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 **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:
+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, which you can access from the navigation menu or using the [global search bar](/explore-analyze/find-and-organize/find-apps-and-objects.md). 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