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[7.x][ML] Machine learning data frame analytics (#43544) (#43592) This merges the initial work that adds a framework for performing machine learning analytics on data frames. The feature is currently experimental and requires a platinum license. Note that the original commits can be found in the `feature-ml-data-frame-analytics` branch. A new set of APIs is added which allows the creation of data frame analytics jobs. Configuration allows specifying different types of analysis to be performed on a data frame. At first there is support for outlier detection. The APIs are: - PUT _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id}/_stats - POST _ml/data_frame/analysis/{id}/_start - POST _ml/data_frame/analysis/{id}/_stop - DELETE _ml/data_frame/analysis/{id} When a data frame analytics job is started a persistent task is created and started. The main steps of the task are: 1. reindex the source index into the dest index 2. analyze the data through the data_frame_analyzer c++ process 3. merge the results of the process back into the destination index In addition, an evaluation API is added which packages commonly used metrics that provide evaluation of various analysis: - POST _ml/data_frame/_evaluate
2019-06-25 13:29:11 -04:00
--
:api: start-data-frame-analytics
:request: StartDataFrameAnalyticsRequest
:response: AcknowledgedResponse
--
[DOCS] Adds missing icons to ML HLRC APIs (#46515)
2019-09-10 11:26:56 -04:00
[role="xpack"]
[7.x][ML] Machine learning data frame analytics (#43544) (#43592) This merges the initial work that adds a framework for performing machine learning analytics on data frames. The feature is currently experimental and requires a platinum license. Note that the original commits can be found in the `feature-ml-data-frame-analytics` branch. A new set of APIs is added which allows the creation of data frame analytics jobs. Configuration allows specifying different types of analysis to be performed on a data frame. At first there is support for outlier detection. The APIs are: - PUT _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id}/_stats - POST _ml/data_frame/analysis/{id}/_start - POST _ml/data_frame/analysis/{id}/_stop - DELETE _ml/data_frame/analysis/{id} When a data frame analytics job is started a persistent task is created and started. The main steps of the task are: 1. reindex the source index into the dest index 2. analyze the data through the data_frame_analyzer c++ process 3. merge the results of the process back into the destination index In addition, an evaluation API is added which packages commonly used metrics that provide evaluation of various analysis: - POST _ml/data_frame/_evaluate
2019-06-25 13:29:11 -04:00
[id="{upid}-{api}"]
[DOCS] Fixes data frame analytics job terminology in HLRC (#46758)
2019-09-16 13:00:44 -04:00
=== Start {dfanalytics-jobs} API
[7.x][ML] Machine learning data frame analytics (#43544) (#43592) This merges the initial work that adds a framework for performing machine learning analytics on data frames. The feature is currently experimental and requires a platinum license. Note that the original commits can be found in the `feature-ml-data-frame-analytics` branch. A new set of APIs is added which allows the creation of data frame analytics jobs. Configuration allows specifying different types of analysis to be performed on a data frame. At first there is support for outlier detection. The APIs are: - PUT _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id}/_stats - POST _ml/data_frame/analysis/{id}/_start - POST _ml/data_frame/analysis/{id}/_stop - DELETE _ml/data_frame/analysis/{id} When a data frame analytics job is started a persistent task is created and started. The main steps of the task are: 1. reindex the source index into the dest index 2. analyze the data through the data_frame_analyzer c++ process 3. merge the results of the process back into the destination index In addition, an evaluation API is added which packages commonly used metrics that provide evaluation of various analysis: - POST _ml/data_frame/_evaluate
2019-06-25 13:29:11 -04:00
[DOCS] Fixes data frame analytics job terminology in HLRC (#46758)
2019-09-16 13:00:44 -04:00
Starts an existing {dfanalytics-job}.
[7.x][ML] Machine learning data frame analytics (#43544) (#43592) This merges the initial work that adds a framework for performing machine learning analytics on data frames. The feature is currently experimental and requires a platinum license. Note that the original commits can be found in the `feature-ml-data-frame-analytics` branch. A new set of APIs is added which allows the creation of data frame analytics jobs. Configuration allows specifying different types of analysis to be performed on a data frame. At first there is support for outlier detection. The APIs are: - PUT _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id}/_stats - POST _ml/data_frame/analysis/{id}/_start - POST _ml/data_frame/analysis/{id}/_stop - DELETE _ml/data_frame/analysis/{id} When a data frame analytics job is started a persistent task is created and started. The main steps of the task are: 1. reindex the source index into the dest index 2. analyze the data through the data_frame_analyzer c++ process 3. merge the results of the process back into the destination index In addition, an evaluation API is added which packages commonly used metrics that provide evaluation of various analysis: - POST _ml/data_frame/_evaluate
2019-06-25 13:29:11 -04:00
It accepts a +{request}+ object and responds with a +{response}+ object.
[id="{upid}-{api}-request"]
[DOCS] Fixes data frame analytics job terminology in HLRC (#46758)
2019-09-16 13:00:44 -04:00
==== Start {dfanalytics-job} request
[7.x][ML] Machine learning data frame analytics (#43544) (#43592) This merges the initial work that adds a framework for performing machine learning analytics on data frames. The feature is currently experimental and requires a platinum license. Note that the original commits can be found in the `feature-ml-data-frame-analytics` branch. A new set of APIs is added which allows the creation of data frame analytics jobs. Configuration allows specifying different types of analysis to be performed on a data frame. At first there is support for outlier detection. The APIs are: - PUT _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id}/_stats - POST _ml/data_frame/analysis/{id}/_start - POST _ml/data_frame/analysis/{id}/_stop - DELETE _ml/data_frame/analysis/{id} When a data frame analytics job is started a persistent task is created and started. The main steps of the task are: 1. reindex the source index into the dest index 2. analyze the data through the data_frame_analyzer c++ process 3. merge the results of the process back into the destination index In addition, an evaluation API is added which packages commonly used metrics that provide evaluation of various analysis: - POST _ml/data_frame/_evaluate
2019-06-25 13:29:11 -04:00
[DOCS] Fixes data frame analytics job terminology in HLRC (#46758)
2019-09-16 13:00:44 -04:00
A +{request}+ object requires a {dfanalytics-job} ID.
[7.x][ML] Machine learning data frame analytics (#43544) (#43592) This merges the initial work that adds a framework for performing machine learning analytics on data frames. The feature is currently experimental and requires a platinum license. Note that the original commits can be found in the `feature-ml-data-frame-analytics` branch. A new set of APIs is added which allows the creation of data frame analytics jobs. Configuration allows specifying different types of analysis to be performed on a data frame. At first there is support for outlier detection. The APIs are: - PUT _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id}/_stats - POST _ml/data_frame/analysis/{id}/_start - POST _ml/data_frame/analysis/{id}/_stop - DELETE _ml/data_frame/analysis/{id} When a data frame analytics job is started a persistent task is created and started. The main steps of the task are: 1. reindex the source index into the dest index 2. analyze the data through the data_frame_analyzer c++ process 3. merge the results of the process back into the destination index In addition, an evaluation API is added which packages commonly used metrics that provide evaluation of various analysis: - POST _ml/data_frame/_evaluate
2019-06-25 13:29:11 -04:00
["source","java",subs="attributes,callouts,macros"]
---------------------------------------------------
include-tagged::{doc-tests-file}[{api}-request]
---------------------------------------------------
[DOCS] Fixes data frame analytics job terminology in HLRC (#46758)
2019-09-16 13:00:44 -04:00
<1> Constructing a new start request referencing an existing {dfanalytics-job}
[7.x][ML] Machine learning data frame analytics (#43544) (#43592) This merges the initial work that adds a framework for performing machine learning analytics on data frames. The feature is currently experimental and requires a platinum license. Note that the original commits can be found in the `feature-ml-data-frame-analytics` branch. A new set of APIs is added which allows the creation of data frame analytics jobs. Configuration allows specifying different types of analysis to be performed on a data frame. At first there is support for outlier detection. The APIs are: - PUT _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id}/_stats - POST _ml/data_frame/analysis/{id}/_start - POST _ml/data_frame/analysis/{id}/_stop - DELETE _ml/data_frame/analysis/{id} When a data frame analytics job is started a persistent task is created and started. The main steps of the task are: 1. reindex the source index into the dest index 2. analyze the data through the data_frame_analyzer c++ process 3. merge the results of the process back into the destination index In addition, an evaluation API is added which packages commonly used metrics that provide evaluation of various analysis: - POST _ml/data_frame/_evaluate
2019-06-25 13:29:11 -04:00
include::../execution.asciidoc[]
[id="{upid}-{api}-response"]
[ML] Return assigned node in start/open job/datafeed response (#55570) Adds a "node" field to the response from the following endpoints: 1. Open anomaly detection job 2. Start datafeed 3. Start data frame analytics job If the job or datafeed is assigned to a node immediately then this field will return the ID of that node. In the case where a job or datafeed is opened or started lazily the node field will contain an empty string. Clients that want to test whether a job or datafeed was opened or started lazily can therefore check for this. Backport of #55473
2020-04-22 07:06:53 -04:00
==== Start {dfanalytics-job} response
[7.x][ML] Machine learning data frame analytics (#43544) (#43592) This merges the initial work that adds a framework for performing machine learning analytics on data frames. The feature is currently experimental and requires a platinum license. Note that the original commits can be found in the `feature-ml-data-frame-analytics` branch. A new set of APIs is added which allows the creation of data frame analytics jobs. Configuration allows specifying different types of analysis to be performed on a data frame. At first there is support for outlier detection. The APIs are: - PUT _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id} - GET _ml/data_frame/analysis/{id}/_stats - POST _ml/data_frame/analysis/{id}/_start - POST _ml/data_frame/analysis/{id}/_stop - DELETE _ml/data_frame/analysis/{id} When a data frame analytics job is started a persistent task is created and started. The main steps of the task are: 1. reindex the source index into the dest index 2. analyze the data through the data_frame_analyzer c++ process 3. merge the results of the process back into the destination index In addition, an evaluation API is added which packages commonly used metrics that provide evaluation of various analysis: - POST _ml/data_frame/_evaluate
2019-06-25 13:29:11 -04:00
[ML] Return assigned node in start/open job/datafeed response (#55570) Adds a "node" field to the response from the following endpoints: 1. Open anomaly detection job 2. Start datafeed 3. Start data frame analytics job If the job or datafeed is assigned to a node immediately then this field will return the ID of that node. In the case where a job or datafeed is opened or started lazily the node field will contain an empty string. Clients that want to test whether a job or datafeed was opened or started lazily can therefore check for this. Backport of #55473
2020-04-22 07:06:53 -04:00
The returned +{response}+ object acknowledges the {dfanalytics-job} has started.
["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests-file}[{api}-response]
--------------------------------------------------
<1> `getNode()` returns the node that the job was assigned to. If the
job is allowed to open lazily and has not yet been assigned to a node
then an empty string is returned. If `getNode()` returns `null` then
the server is an old version that does not return node information.