From a0f41fcd3f8a110eb0c685639d534f627e9363a0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Istv=C3=A1n=20Zolt=C3=A1n=20Szab=C3=B3?= Date: Thu, 1 Aug 2019 14:32:08 +0200 Subject: [PATCH] [DOCS] Updates ML/anomaly detection terms in the Kibana guide from 7.1 to 6.5 (#42428) * [DOCS] Updates ML/anomaly detection terms in the Kibana guide from 7.1 to 6.5. * [DOCS] Fixes indentation. --- docs/ml/creating-jobs.asciidoc | 8 ++++---- docs/ml/index.asciidoc | 33 +++++++++++++++++---------------- 2 files changed, 21 insertions(+), 20 deletions(-) diff --git a/docs/ml/creating-jobs.asciidoc b/docs/ml/creating-jobs.asciidoc index d88df4d8e14e8..5b2f9cd54ce1c 100644 --- a/docs/ml/creating-jobs.asciidoc +++ b/docs/ml/creating-jobs.asciidoc @@ -1,8 +1,8 @@ [role="xpack"] [[ml-jobs]] -== Creating machine learning jobs +== Creating {anomaly-jobs} -Machine learning jobs contain the configuration information and metadata +{anomaly-jobs-cap} contain the configuration information and metadata necessary to perform an analytics task. {kib} provides the following wizards to make it easier to create jobs: @@ -42,7 +42,7 @@ activity on your systems, the following wizards appear: [role="screenshot"] image::ml/images/ml-data-recognizer-auditbeat.jpg[A screenshot of the {auditbeat} job creation wizards] -These wizards create {ml} jobs, dashboards, searches, and visualizations that +These wizards create {anomaly-jobs}, dashboards, searches, and visualizations that are customized to help you analyze your {auditbeat} and {filebeat} data. If you are not certain which type of job to create, you can use the @@ -53,7 +53,7 @@ a time field, it can identify possible fields for {ml} analysis. =============================== If your data is located outside of {es}, you cannot use {kib} to create your jobs and you cannot use {dfeeds} to retrieve your data in real time. -Machine learning analysis is still possible, however, by using APIs to +{anomaly-detect-cap} is still possible, however, by using APIs to create and manage jobs and post data to them. For more information, see {ref}/ml-apis.html[Machine Learning APIs]. =============================== diff --git a/docs/ml/index.asciidoc b/docs/ml/index.asciidoc index a7571be6d70fd..15f3fbab2b4a9 100644 --- a/docs/ml/index.asciidoc +++ b/docs/ml/index.asciidoc @@ -1,35 +1,36 @@ [role="xpack"] [[xpack-ml]] -= Machine Learning += {ml-cap} [partintro] -- As datasets increase in size and complexity, the human effort required to inspect dashboards or maintain rules for spotting infrastructure problems, -cyber attacks, or business issues becomes impractical. The Elastic {ml-features} -automatically model the normal behavior of your time series data — learning -trends, periodicity, and more — in real time to identify anomalies, streamline -root cause analysis, and reduce false positives. +cyber attacks, or business issues becomes impractical. The Elastic {ml} +{anomaly-detect} feature automatically model the normal behavior of your time +series data — learning trends, periodicity, and more — in real time to identify +anomalies, streamline root cause analysis, and reduce false positives. -The {ml-features} run in and scale with {es}, and include an -intuitive UI on the {kib} *Machine Learning* page for creating anomaly detection -jobs and understanding results. +{anomaly-detect-cap} run in and scale with {es}, and include an +intuitive UI on the {kib} *Machine Learning* page for creating {anomaly-jobs} +and understanding results. If you have a basic license, you can use the *Data Visualizer* to learn more about your data. In particular, if your data is stored in {es} and contains a time field, you can use the *Data Visualizer* to identify possible fields for -{ml} analysis: +{anomaly-detect}: [role="screenshot"] image::ml/images/ml-data-visualizer-sample.jpg[Data Visualizer for sample flight data] experimental[] You can also upload a CSV, NDJSON, or log file (up to 100 MB in size). -The {ml-features} identify the file format and field mappings. You can then +The *Data Visualizer* identifies the file format and field mappings. You can then optionally import that data into an {es} index. -If you have a trial or platinum license, you can <> -and manage jobs and {dfeeds} from the *Job Management* pane: +If you have a trial or platinum license, you can +<> and manage jobs and {dfeeds} from the *Job +Management* pane: [role="screenshot"] image::ml/images/ml-job-management.jpg[Job Management] @@ -42,7 +43,7 @@ You can use the *Settings* pane to create and edit image::ml/images/ml-settings.jpg[Calendar Management] The *Anomaly Explorer* and *Single Metric Viewer* display the results of your -{ml} jobs. For example: +{anomaly-jobs}. For example: [role="screenshot"] image::ml/images/ml-single-metric-viewer.jpg[Single Metric Viewer] @@ -56,7 +57,7 @@ occurring in your operational environment at that time: image::ml/images/ml-annotations-list.jpg[Single Metric Viewer with annotations] In some circumstances, annotations are also added automatically. For example, if -the {ml} analytics detect that there is missing data, it annotates the affected +the {anomaly-job} detects that there is missing data, it annotates the affected time period. For more information, see {stack-ov}/ml-delayed-data-detection.html[Handling delayed data]. The *Job Management* pane shows the full list of annotations for each job. @@ -65,8 +66,8 @@ NOTE: The {kib} {ml-features} use pop-ups. You must configure your web browser so that it does not block pop-up windows or create an exception for your {kib} URL. -For more information about {ml}, see -{stack-ov}/xpack-ml.html[Machine learning in the {stack}]. +For more information about the {anomaly-detect} feature, see +{stack-ov}/xpack-ml.html[{ml-cap} {anomaly-detect}]. --