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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] Updates dataframe transform terminology (#46642)
2019-09-16 11:28:19 -04:00
=== Start {dfanalytics} 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] Updates dataframe transform terminology (#46642)
2019-09-16 11:28:19 -04:00
The start {dfanalytics} API is used to start an existing {dataframe-analytics-config}.
[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] Updates dataframe transform terminology (#46642)
2019-09-16 11:28:19 -04:00
==== Start {dfanalytics} 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
A +{request}+ object requires a {dataframe-analytics-config} id.
["source","java",subs="attributes,callouts,macros"]
---------------------------------------------------
include-tagged::{doc-tests-file}[{api}-request]
---------------------------------------------------
<1> Constructing a new start request referencing an existing {dataframe-analytics-config}
include::../execution.asciidoc[]
[id="{upid}-{api}-response"]
==== Response
[DOCS] Updates dataframe transform terminology (#46642)
2019-09-16 11:28:19 -04:00
The returned +{response}+ object acknowledges the {dfanalytics-job} has started.