From 0100407bf63a76cf0727b506e2014afd3d05053a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Istv=C3=A1n=20Zolt=C3=A1n=20Szab=C3=B3?= Date: Tue, 8 Aug 2023 13:49:02 +0200 Subject: [PATCH] [DOCS] Updates ML getting started (#2505) (cherry picked from commit 3936a9109bde90d2fbeefe5fca12fa80b7c6c4f4) --- .../get-started/images/ml-gs-data-keyword.jpg | Bin 379296 -> 533122 bytes .../get-started/images/ml-gs-data-metric.jpg | Bin 432902 -> 686972 bytes .../get-started/ml-getting-started.asciidoc | 3 ++- .../ml/get-started/ml-gs-forecasts.asciidoc | 4 ++-- .../stack/ml/get-started/ml-gs-jobs.asciidoc | 4 ++-- .../ml/get-started/ml-gs-results.asciidoc | 11 ++++++----- .../ml/get-started/ml-gs-visualizer.asciidoc | 17 ++++++++++------- 7 files changed, 22 insertions(+), 17 deletions(-) diff --git a/docs/en/stack/ml/get-started/images/ml-gs-data-keyword.jpg b/docs/en/stack/ml/get-started/images/ml-gs-data-keyword.jpg index 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See <>. . {kibana-ref}/get-started.html#gs-get-data-into-kibana[Add the sample data sets that ship with {kib}]. -.. From the {kib} home page, click *Add data*, then select *Sample data*. +.. From the {kib} home page, click *Try samle data*, then open the +*Other sample data sets* section. .. Pick a data set. In this tutorial, you'll use the *Sample web logs*. While you're here, feel free to click *Add data* on all of the available sample data diff --git a/docs/en/stack/ml/get-started/ml-gs-forecasts.asciidoc b/docs/en/stack/ml/get-started/ml-gs-forecasts.asciidoc index 922abc0ea..d7e8b77d2 100644 --- a/docs/en/stack/ml/get-started/ml-gs-forecasts.asciidoc +++ b/docs/en/stack/ml/get-started/ml-gs-forecasts.asciidoc @@ -9,8 +9,8 @@ In addition to detecting anomalous behavior in your data, you can use the To create a forecast in {kib}: . View your job results (for example, for the `low_request_rate` job) in the -**Single Metric Viewer**. To find that view, follow the link in the **Actions** -column on the **Anomaly Detection** page. +**Single Metric Viewer**. To find that view, click the **View series** button in +the **Actions** column on the **Anomaly Detection** page. . Click **Forecast**. + diff --git a/docs/en/stack/ml/get-started/ml-gs-jobs.asciidoc b/docs/en/stack/ml/get-started/ml-gs-jobs.asciidoc index c0049c6ba..f2087c692 100644 --- a/docs/en/stack/ml/get-started/ml-gs-jobs.asciidoc +++ b/docs/en/stack/ml/get-started/ml-gs-jobs.asciidoc @@ -41,8 +41,8 @@ For more information, see <>, <>, and **** 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* > *Job Management* -page. Alternatively, you can see the configuration files in +you can do so on the *Machine Learning* > *Anomaly Detection* > *Jobs* page. +Alternatively, you can see the configuration files in https://github.com/elastic/kibana/tree/{branch}/x-pack/plugins/ml/server/models/data_recognizer/modules/sample_data_weblogs[GitHub ]. For the purposes of this tutorial, however, here's a quick overview of the goal of each job: diff --git a/docs/en/stack/ml/get-started/ml-gs-results.asciidoc b/docs/en/stack/ml/get-started/ml-gs-results.asciidoc index 0bdbbc8e9..489053679 100644 --- a/docs/en/stack/ml/get-started/ml-gs-results.asciidoc +++ b/docs/en/stack/ml/get-started/ml-gs-results.asciidoc @@ -154,11 +154,11 @@ Let's start by looking at the `response_code_rates` job in the . Select the *Anomaly Detection* tab in *{ml-app}* to see the list of your {anomaly-jobs}. -. Click the grid icon in the *Actions* column for your `response_code_rates` job -to view its results in the **Anomaly Explorer**. +. Open the `response_code_rates` job in the Anomaly Explorer to view its results + by clicking the corresponding icon in the row of the job. -For this particular job, you can choose to see separate swim lanes for each client -IP or response code. For example: +For this particular job, you can choose to see separate swim lanes for each +client IP or response code. For example: [role="screenshot"] image::images/ml-gs-job2-explorer.jpg["Anomaly explorer for response_code_rates job"] @@ -191,7 +191,8 @@ image::images/ml-gs-job2-explorer-anomaly.jpg["Anomaly charts for the response_c You can see exact times when anomalies occurred. If there are multiple detectors or metrics in the job, you can see which caught the anomaly. You can also switch -to viewing this time series in the **Single Metric Viewer**. +to viewing this time series in the **Single Metric Viewer** by clicking the +**View Series** button in the **Actions** menu. Below the charts, there is a table that provides more information, such as the typical and actual values and the influencers that contributed to the anomaly. diff --git a/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc b/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc index c105d1f59..a1dcc9da6 100644 --- a/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc +++ b/docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc @@ -17,19 +17,22 @@ exception for your {kib} URL. -- -. Click *Machine Learning* in the side navigation. +. Click *Machine Learning* in the {kib} main menu. . Select the *{data-viz}* tab. -. Click *Select index* and choose the `kibana_sample_data_logs` {data-source}. +. Click *Select data view* and choose the `kibana_sample_data_logs` {data-source}. . Use the time filter to select a time period that you're interested in exploring. Alternatively, click -*Use full kibana_sample_data_logs data* to view the full time range of data. - -. Optional: Change the sample size, which is the number of documents per shard -that are used in the {data-viz}. There is a relatively small number of -documents in the {kib} sample data, so you can choose a value of `all`. For +*Use full data* to view the full time range of data. + +. Optional: You can change the random sampling behavior, which affects the +number of documents per shard that are used in the {data-viz}. You can use +automatic random sampling that balances accuracy and speed, manual sampling +where you can chose a value for the sampling percentage, or you can turn the +feaure off to use the full data set. There is a relatively small number of +documents in the {kib} sample data, so you can turn random sampling off. For larger data sets, keep in mind that using a large sample size increases query run times and increases the load on the cluster.

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