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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: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-ad-forecast.md b/explore-analyze/machine-learning/anomaly-detection/ml-ad-forecast.md index 33444c8815..575eb3a377 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-ad-forecast.md +++ b/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: ::: diff --git a/explore-analyze/machine-learning/anomaly-detection/ml-ad-view-results.md b/explore-analyze/machine-learning/anomaly-detection/ml-ad-view-results.md index 9fc9d872e4..f3c5db063f 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-ad-view-results.md +++ b/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: ::: 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 f3e9e15e30..ff188363a1 100644 --- a/explore-analyze/machine-learning/anomaly-detection/ml-getting-started.md +++ b/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.