[ML UI][9.1 & Serverless] Update Machine Learning docs for new anomaly detection severity colors and filtering controls

explore-analyze/machine-learning/anomaly-detection/ml-ad-explain.md

@@ -55,7 +55,7 @@ Two more factors may lead to a reduction of the initial score: a high variance i
 
 Real-world anomalies often show the impacts of several factors. The **Anomaly explanation** section in the Single Metric Viewer can help you interpret an anomaly in its context.
 
-:::{image} /explore-analyze/images/machine-learning-detailed-single-metric.jpg
+:::{image} /explore-analyze/images/machine-learning-detailed-single-metric.png
 :alt: Detailed view of the Single Metric Viewer in {{kib}}
 :screenshot:
 :::

explore-analyze/machine-learning/anomaly-detection/ml-ad-forecast.md

@@ -20,7 +20,7 @@ You can also use it to estimate the probability of a time series value occurring
 
 Each forecast has a unique ID, which you can use to distinguish between forecasts that you created at different times. You can create a forecast by using the [forecast {{anomaly-jobs}} API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-forecast) or by using {{kib}}. For example:
 
-:::{image} /explore-analyze/images/machine-learning-overview-forecast.jpg
+:::{image} /explore-analyze/images/machine-learning-overview-forecast.png
 :alt: Example screenshot from the Machine Learning Single Metric Viewer in Kibana
 :screenshot:
 :::

explore-analyze/machine-learning/anomaly-detection/ml-ad-view-results.md

@@ -51,7 +51,7 @@ The influencer results show which entities were anomalous and when. One influenc
 
 For example, the `high_sum_total_sales` {{anomaly-job}} for the eCommerce orders sample data uses `customer_full_name.keyword` and `category.keyword` as influencers. You can examine the influencer results with the [get influencers API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ml-get-influencers). Alternatively, you can use the **Anomaly Explorer** in {{kib}}:
 
-:::{image} /explore-analyze/images/machine-learning-influencers.jpg
+:::{image} /explore-analyze/images/machine-learning-influencers.png
 :alt: Influencers in the {{kib}} Anomaly Explorer
 :screenshot:
 :::

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

@@ -117,7 +117,7 @@ After the {{dfeeds}} are started and the {{anomaly-jobs}} have processed some da
 Depending on the capacity of your machine, you might need to wait a few seconds for the {{ml}} analysis to generate initial results.
 ::::
 
-:::{image} /explore-analyze/images/machine-learning-ml-gs-web-results.jpg
+:::{image} /explore-analyze/images/machine-learning-ml-gs-web-results.png
 :alt: Create jobs for the sample web logs
 :screenshot:
 :::
@@ -132,7 +132,7 @@ One of the sample jobs (`low_request_rate`), is a *single metric {{anomaly-job}}
 
 Let’s start by looking at this simple job in the **Single Metric Viewer**:
 
-1. Select the **Jobs** tab in **{{ml-app}}** to see the list of your {{anomaly-jobs}}.
+1. Select the **Anomaly Detection Jobs** tab in **{{ml-app}}** to see the list of your {{anomaly-jobs}}.
 2. Click the chart icon in the **Actions** column for your `low_request_rate` job to view its results in the **Single Metric Viewer**.
 3. Use the relative mode of the date picker to select a start date one week in the past and an end date one month in the future to cover the majority of the analyzed data points.