From fa968cc6d1ae260e9e2bf720dbe8d84d9e35a354 Mon Sep 17 00:00:00 2001 From: Lisa Cawley Date: Tue, 17 Sep 2019 14:44:55 -0700 Subject: [PATCH] [DOCS] Move machine learning details out of Kibana Guide (#45855) (#45959) --- docs/redirects.asciidoc | 15 +++ docs/user/extend.asciidoc | 12 ++ docs/user/index.asciidoc | 2 +- docs/user/ml/creating-df-kib.asciidoc | 50 ------- docs/user/ml/creating-jobs.asciidoc | 83 ------------ docs/user/ml/images/ml-create-job.jpg | Bin 119352 -> 0 bytes .../images/ml-data-recognizer-auditbeat.jpg | Bin 208598 -> 0 bytes .../ml/images/ml-data-recognizer-filebeat.jpg | Bin 194793 -> 0 bytes .../images/ml-data-recognizer-metricbeat.jpg | Bin 159411 -> 0 bytes .../ml/images/ml-data-recognizer-sample.jpg | Bin 74124 -> 0 bytes docs/user/ml/images/ml-definepivot.jpg | Bin 327021 -> 0 bytes docs/user/ml/images/ml-jobid.jpg | Bin 242541 -> 0 bytes docs/user/ml/index.asciidoc | 14 +- docs/user/ml/job-tips.asciidoc | 124 ------------------ 14 files changed, 31 insertions(+), 269 deletions(-) create mode 100644 docs/user/extend.asciidoc delete mode 100644 docs/user/ml/creating-df-kib.asciidoc delete mode 100644 docs/user/ml/creating-jobs.asciidoc delete mode 100644 docs/user/ml/images/ml-create-job.jpg delete mode 100644 docs/user/ml/images/ml-data-recognizer-auditbeat.jpg delete mode 100644 docs/user/ml/images/ml-data-recognizer-filebeat.jpg delete mode 100644 docs/user/ml/images/ml-data-recognizer-metricbeat.jpg delete mode 100644 docs/user/ml/images/ml-data-recognizer-sample.jpg delete mode 100644 docs/user/ml/images/ml-definepivot.jpg delete mode 100644 docs/user/ml/images/ml-jobid.jpg delete mode 100644 docs/user/ml/job-tips.asciidoc diff --git a/docs/redirects.asciidoc b/docs/redirects.asciidoc index a8d8a66fd182c..3564259ae1277 100644 --- a/docs/redirects.asciidoc +++ b/docs/redirects.asciidoc @@ -35,3 +35,18 @@ This page has moved. Please see <>. This page has moved. Please see <>. +[role="exclude",id="creating-df-kib"] +== Creating {transforms} + +This page is deleted. Please see +{stack-ov}/ecommerce-dataframes.html[Transforming the eCommerce sample data]. + +[role="exclude",id="ml-jobs"] +== Creating {anomaly-jobs} + +This page has moved. Please see {stack-ov}/create-jobs.html[Creating {anomaly-jobs}]. + +[role="exclude",id="job-tips"] +== Machine learning job tips + +This page has moved. Please see {stack-ov}/job-tips.html[Machine learning job tips]. diff --git a/docs/user/extend.asciidoc b/docs/user/extend.asciidoc new file mode 100644 index 0000000000000..3f89b364779fe --- /dev/null +++ b/docs/user/extend.asciidoc @@ -0,0 +1,12 @@ +[[extend]] += Extend your use case + +[partintro] +-- +//TBD + +* <> + +-- + +include::ml/index.asciidoc[] diff --git a/docs/user/index.asciidoc b/docs/user/index.asciidoc index bd90a81e5a9c5..80e37e7cd242e 100644 --- a/docs/user/index.asciidoc +++ b/docs/user/index.asciidoc @@ -16,7 +16,7 @@ include::dashboard.asciidoc[] include::canvas.asciidoc[] -include::ml/index.asciidoc[] +include::extend.asciidoc[] include::{kib-repo-dir}/maps/index.asciidoc[] diff --git a/docs/user/ml/creating-df-kib.asciidoc b/docs/user/ml/creating-df-kib.asciidoc deleted file mode 100644 index 98fb772845a03..0000000000000 --- a/docs/user/ml/creating-df-kib.asciidoc +++ /dev/null @@ -1,50 +0,0 @@ -[role="xpack"] -[[creating-df-kib]] -== Creating {dataframe-transforms} - -beta[] - -You can create {stack-ov}/ml-dataframes.html[{dataframe-transforms}] in the -{kib} Machine Learning application. - -[role="screenshot"] -image::user/ml/images/ml-definepivot.jpg["Defining a {dataframe} pivot"] - -Select the index pattern or saved search you want to transform. To pivot your -data, you must group the data by at least one field and apply at least one -aggregation. The {dataframe} pivot preview on the right side provides a visual -verification. - -Once you have created the pivot, add a job ID and define the index for the -transformed data (_target index_). If the target index does not exist, it will be -created automatically. You can optionally select to create a {kib} index pattern -for the target index. At the end of the process, a {dataframe} job is created as -a result. - -[role="screenshot"] -image::user/ml/images/ml-jobid.jpg["Job ID and target index"] - -After you create {dataframe} jobs, you can start, stop, and delete them -and explore their progress and statistics from the jobs list. - -For a more detailed example of using {dataframes} with the {kib} sample data, -see {stack-ov}/ecommerce-dataframes.html[Transforming your data]. - -[NOTE] -=============================== -If {stack} {security-features} are enabled, you must have appropriate authority -to work with {dataframes}. For example, there are built-in -`data_frame_transforms_admin` and `data_frame_transforms_user` roles that have -`manage_data_frame_transforms` and `monitor_data_frame_transforms` cluster -privileges respectively. See -{stack-ov}/built-in-roles.html[Built-in roles] and -{stack-ov}/security-privileges.html[Security privileges]. - -Depending on what tasks you perform, you might require additional privileges. -For example, to create a {dataframe-transform} and generate a new target index, -you need `manage_data_frame_transforms` cluster privileges, `read` and -`view_index_metadata` privileges on the source index, and `read`, `create_index`, -and `index` privileges on the target index. For more information, see the -authorization details for each {ref}/data-frame-apis.html[{dataframe} API]. - -=============================== \ No newline at end of file diff --git a/docs/user/ml/creating-jobs.asciidoc b/docs/user/ml/creating-jobs.asciidoc deleted file mode 100644 index 8eb1ab29897cc..0000000000000 --- a/docs/user/ml/creating-jobs.asciidoc +++ /dev/null @@ -1,83 +0,0 @@ -[role="xpack"] -[[ml-jobs]] -== Creating {anomaly-jobs} - -{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: - -[role="screenshot"] -image::user/ml/images/ml-create-job.jpg[Create New Job] - -A _single metric job_ is a simple job that contains a single _detector_. A -detector defines the type of analysis that will occur and which fields to -analyze. In addition to limiting the number of detectors, the single metric job -creation wizard omits many of the more advanced configuration options. - -A _multi-metric job_ can contain more than one detector, which is more efficient -than running multiple jobs against the same data. - -A _population job_ detects activity that is unusual compared to the behavior of -the population. For more information, see -{stack-ov}/ml-configuring-pop.html[Performing population analysis]. - -An _advanced job_ can contain multiple detectors and enables you to configure all -job settings. - -{kib} can also recognize certain types of data and provide specialized wizards -for that context. For example, if you -<>, the following wizard -appears: - -[role="screenshot"] -image::user/ml/images/ml-data-recognizer-sample.jpg[A screenshot of the {kib} sample data web log job creation wizard] - -TIP: Alternatively, after you load a sample data set on the {kib} home page, you can click *View data* > *ML jobs*. There are {anomaly-jobs} for both the sample eCommerce orders data set and the sample web logs data set. - -If you use {filebeat-ref}/index.html[{filebeat}] -to ship access logs from your -http://nginx.org/[Nginx] and https://httpd.apache.org/[Apache] HTTP servers to -{es} and store it using fields and datatypes from the -{ecs-ref}/ecs-reference.html[Elastic Common Schema (ECS)], the following wizards -appear: - -[role="screenshot"] -image::user/ml/images/ml-data-recognizer-filebeat.jpg[A screenshot of the {filebeat} job creation wizards] - -If you use {auditbeat-ref}/index.html[{auditbeat}] to audit process -activity on your systems, the following wizards appear: - -[role="screenshot"] -image::user/ml/images/ml-data-recognizer-auditbeat.jpg[A screenshot of the {auditbeat} job creation wizards] - -Likewise, if you use the {metricbeat-ref}/metricbeat-module-system.html[{metricbeat} system module] to monitor your servers, the following -wizards appear: - -[role="screenshot"] -image::user/ml/images/ml-data-recognizer-metricbeat.jpg[A screenshot of the {metricbeat} job creation wizards] - -These wizards create {anomaly-jobs}, dashboards, searches, and visualizations -that are customized to help you analyze your {auditbeat}, {filebeat}, and -{metricbeat} data. - -[NOTE] -=============================== -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. -{anomal-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[{ml-cap} {anomaly-detect} APIs]. -=============================== - -//// -Ready to get some hands-on experience? See -{stack-ov}/ml-getting-started.html[Getting Started with Machine Learning]. - -The following video tutorials also demonstrate single metric, multi-metric, and -advanced jobs: - -* https://www.elastic.co/videos/machine-learning-tutorial-creating-a-single-metric-job[Machine Learning for the Elastic Stack: Creating a single metric job] -* https://www.elastic.co/videos/machine-learning-tutorial-creating-a-multi-metric-job[Machine Learning for the Elastic Stack: Creating a multi-metric job] -* https://www.elastic.co/videos/machine-learning-lab-3-detect-outliers-in-a-population[Machine Learning for the Elastic Stack: Detect Outliers in a Population] -//// \ No newline at end of file diff --git a/docs/user/ml/images/ml-create-job.jpg b/docs/user/ml/images/ml-create-job.jpg deleted file mode 100644 index 5b938633c9b858f5a41e853cf472078a056e24a7..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 119352 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zly#Nk7PE9oj=k}PE2@Lx=#9fyk=jZlx02XAFZkF50}$i-$iIw=kW&Q7UR{6aF12!Y`~Ad<^QI zDnqxbBJ4}CG4&7L!zP_eZxJ=i!zMdDT%8&`i^={drI_sEUl((dZeCvUNq(EV^zjSE z;<|I8=ci-C@`m{=N1eBM7|B`mW8t~9LK!W$;z9|@vyKcqnJ!ME13vTGnEDLtGqp@$^k6SH4lKh}T(>@JDfmOG=|E30}UqU#C?D#xWCb z30esri>;4?d~!6K=*|FhSzIjeXns*&%)pN9dr|`tyTu<|v-Te3MOa@;o^(G^kz?fI z^s68&PLtUYa+x17MOmP|-*?)(?VQO;4fP}F_pwsXT-jz7X5*hq)n7WNvVjDzbJxIm zW|@wvgxRylFW)#|&>vJb8x}MgVEx>WyEuNU>?1CZ?HKc9@dq)T()RZ!8B9!g891JE zG~KTjW}#0Cv(`h&FpAcY5{T)0U;c_3l476F9$LGRIp_KSF&q1=yzurqm))Dl@q|VAsUZhCW@BsJo>5q(40F`sr;A9V@N3dmKDGVUE*P)H&9qK67^r?`0pxACbx$F>E$&lh9;mvv_@aC_qs0 z&Y-CLQdNnC)*Zd;XWFxAT!%E9+DOJi7pq8NvCMI9t{WP+l2v9*GJ7ExC-o;ndM};7 zBRZAQ_`NtYO7fp;!hc@>r9S>g7Mfg#07z{b37|ELd=|*`OdycyFV=Xb88X4W=brNi!%XT`m?ny}7 za4IHDmZZ@{qSbyErih39iUF4t`PIg+&Fv|eTfSYZ*Z0c1!nf)Avuns}?d|(W2zhzi z@HD*#eOSZe>8~I$l2)Q}Yq)};JLvnoXFz>&yM*n!u5#P=pf-HDP{x&N97%&?dVzCt z%1VMpmg&mhFBtHlng;FXn?}=(i6fKS8r#|tgmtYPN!c}BNR1;RbS8dmumgQ+J+0}0 z0pP)PZ=8C@=7jV(QQK%@8%ws-Y`jplt)R31P9TZ5=UF%qEuNX8dbP5Wku#P3!~1s9 z?VJ3nc6&w^$z=cDFpU2Ew?JB-+1pit|de) znrRhO@1>_MNol}12QlHvpbJ^6Hgb`ZTd38!et*&1mqEq=tw~`owldwT7O(O< z7vsPEr{?&l=J;RLf!`BK(}1lAm)QHzzyS_fvG$+FQ%Q#Jus7r171yFU72;ZH_%ng> zfun(s+5vqN`4jm32>|~QIsgGsL~lvvkROXzL!WUyap^;F15~BQ>5KHF#Y-b5u*kRC zkD4TeV> and manage jobs and {dfeeds} from the *Job +create {anomaly-jobs} and manage jobs and {dfeeds} from the *Job Management* pane: [role="screenshot"] @@ -67,11 +64,6 @@ browser so that it does not block pop-up windows or create an exception for your {kib} URL. For more information about the {anomaly-detect} feature, see +https://www.elastic.co/what-is/elastic-stack-machine-learning and {stack-ov}/xpack-ml.html[{ml-cap} {anomaly-detect}]. --- - -include::creating-jobs.asciidoc[] -include::job-tips.asciidoc[] -include::creating-df-kib.asciidoc[] - diff --git a/docs/user/ml/job-tips.asciidoc b/docs/user/ml/job-tips.asciidoc deleted file mode 100644 index 3451d45bd17aa..0000000000000 --- a/docs/user/ml/job-tips.asciidoc +++ /dev/null @@ -1,124 +0,0 @@ -[role="xpack"] -[[job-tips]] -=== Machine learning job tips -++++ -Job tips -++++ - -When you create an {anomaly-job} in {kib}, the job creation wizards can -provide advice based on the characteristics of your data. By heeding these -suggestions, you can create jobs that are more likely to produce insightful {ml} -results. - -[[bucket-span]] -==== Bucket span - -The bucket span is the time interval that {ml} analytics use to summarize and -model data for your job. When you create an {anomaly-job} in {kib}, you can -choose to estimate a bucket span value based on your data characteristics. - -NOTE: The bucket span must contain a valid time interval. For more information, -see {ref}/ml-job-resource.html#ml-analysisconfig[Analysis configuration objects]. - -If you choose a value that is larger than one day or is significantly different -than the estimated value, you receive an informational message. For more -information about choosing an appropriate bucket span, see -{stack-ov}/ml-buckets.html[Buckets]. - -[[cardinality]] -==== Cardinality - -If there are logical groupings of related entities in your data, {ml} analytics -can make data models and generate results that take these groupings into -consideration. For example, you might choose to split your data by user ID and -detect when users are accessing resources differently than they usually do. - -If the field that you use to split your data has many different values, the -job uses more memory resources. In particular, if the cardinality of the -`by_field_name`, `over_field_name`, or `partition_field_name` is greater than -1000, you are advised that there might be high memory usage. - -Likewise if you are performing population analysis and the cardinality of the -`over_field_name` is below 10, you are advised that this might not be a suitable -field to use. For more information, see -{stack-ov}/ml-configuring-pop.html[Performing Population Analysis]. - -[[detectors]] -==== Detectors - -Each {anomaly-job} must have one or more _detectors_. A detector applies an -analytical function to specific fields in your data. If your job does not -contain a detector or the detector does not contain a -{stack-ov}/ml-functions.html[valid function], you receive an error. - -If a job contains duplicate detectors, you also receive an error. Detectors are -duplicates if they have the same `function`, `field_name`, `by_field_name`, -`over_field_name` and `partition_field_name`. - -[[influencers]] -==== Influencers - -When you create an {anomaly-job}, you can specify _influencers_, which are also -sometimes referred to as _key fields_. Picking an influencer is strongly -recommended for the following reasons: - -* It allows you to more easily assign blame for the anomaly -* It simplifies and aggregates the results - -The best influencer is the person or thing that you want to blame for the -anomaly. In many cases, users or client IP addresses make excellent influencers. -Influencers can be any field in your data; they do not need to be fields that -are specified in your detectors, though they often are. - -As a best practice, do not pick too many influencers. For example, you generally -do not need more than three. If you pick many influencers, the results can be -overwhelming and there is a small overhead to the analysis. - -The job creation wizards in {kib} can suggest which fields to use as influencers. - -[[model-memory-limits]] -==== Model memory limits - -For each {anomaly-job}, you can optionally specify a `model_memory_limit`, which -is the approximate maximum amount of memory resources that are required for -analytical processing. The default value is 1 GB. Once this limit is approached, -data pruning becomes more aggressive. Upon exceeding this limit, new entities -are not modeled. - -You can also optionally specify the `xpack.ml.max_model_memory_limit` setting. -By default, it's not set, which means there is no upper bound on the acceptable -`model_memory_limit` values in your jobs. - -TIP: If you set the `model_memory_limit` too high, it will be impossible to open -the job; jobs cannot be allocated to nodes that have insufficient memory to run -them. - -If the estimated model memory limit for an {anomaly-job} is greater than the -model memory limit for the job or the maximum model memory limit for the cluster, -the job creation wizards in {kib} generate a warning. If the estimated memory -requirement is only a little higher than the `model_memory_limit`, the job will -probably produce useful results. Otherwise, the actions you take to address -these warnings vary depending on the resources available in your cluster: - -* If you are using the default value for the `model_memory_limit` and the {ml} -nodes in the cluster have lots of memory, the best course of action might be to -simply increase the job's `model_memory_limit`. Before doing this, however, -double-check that the chosen analysis makes sense. The default -`model_memory_limit` is relatively low to avoid accidentally creating a job that -uses a huge amount of memory. -* If the {ml} nodes in the cluster do not have sufficient memory to accommodate -a job of the estimated size, the only options are: -** Add bigger {ml} nodes to the cluster, or -** Accept that the job will hit its memory limit and will not necessarily find -all the anomalies it could otherwise find. - -If you are using {ece} or the hosted Elasticsearch Service on Elastic Cloud, -`xpack.ml.max_model_memory_limit` is set to prevent you from creating jobs -that cannot be allocated to any {ml} nodes in the cluster. If you find that you -cannot increase `model_memory_limit` for your {ml} jobs, the solution is to -increase the size of the {ml} nodes in your cluster. - -For more information about the `model_memory_limit` property and the -`xpack.ml.max_model_memory_limit` setting, see -{ref}/ml-job-resource.html#ml-analysisconfig[Analysis limits] and -{ref}/ml-settings.html[Machine learning settings]. \ No newline at end of file