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[ML] adding new _preview endpoint for data frame analytics #69453

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benwtrent
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This commit adds a new _preview endpoint for data frame analytics.

This allows users to see the data on which their model will be trained. This is especially useful
in the arrival of custom feature processors.

The API design is a similar to datafeed _preview and data frame analytics _explain.

@elasticmachine elasticmachine added the Team:ML Meta label for the ML team label Feb 23, 2021
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Pinging @elastic/ml-core (Team:ML)

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@dimitris-athanasiou dimitris-athanasiou left a comment

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Good stuff! Just a comment on reusing the PutDataFrameAnalyticsAction.Request.

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LGTM

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@elasticmachine update branch

@benwtrent benwtrent merged commit 2279caf into elastic:master Mar 1, 2021
@benwtrent benwtrent deleted the feature/ml-df-add-_feature_preview-api branch March 1, 2021 17:25
benwtrent added a commit to benwtrent/elasticsearch that referenced this pull request Mar 1, 2021
…9453)

This commit adds a new `_preview` endpoint for data frame analytics.

This allows users to see the data on which their model will be trained. This is especially useful
in the arrival of custom feature processors.

The API design is a similar to datafeed `_preview` and data frame analytics `_explain`.
# Conflicts:
#	x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/MachineLearning.java
benwtrent added a commit that referenced this pull request Mar 1, 2021
) (#69729)

* [ML] adding new _preview endpoint for data frame analytics (#69453)

This commit adds a new `_preview` endpoint for data frame analytics.

This allows users to see the data on which their model will be trained. This is especially useful
in the arrival of custom feature processors.

The API design is a similar to datafeed `_preview` and data frame analytics `_explain`.
easyice pushed a commit to easyice/elasticsearch that referenced this pull request Mar 25, 2021
…9453)

This commit adds a new `_preview` endpoint for data frame analytics. 

This allows users to see the data on which their model will be trained. This is especially useful 
in the arrival of custom feature processors.

The API design is a similar to datafeed `_preview` and data frame analytics `_explain`.
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5 participants