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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`.
36 lines
1.2 KiB
Text
36 lines
1.2 KiB
Text
[role="xpack"]
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[testenv="platinum"]
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[[ml-df-analytics-apis]]
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= {ml-cap} {dfanalytics} APIs
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You can use the following APIs to perform {ml} {dfanalytics} activities.
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* <<preview-dfanalytics,Preview {dfanalytics}>>
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* <<put-dfanalytics,Create {dfanalytics-jobs}>>
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* <<update-dfanalytics,Update {dfanalytics-jobs}>>
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* <<delete-dfanalytics,Delete {dfanalytics-jobs}>>
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* <<get-dfanalytics,Get {dfanalytics-jobs} info>>
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* <<get-dfanalytics-stats,Get {dfanalytics-jobs} statistics>>
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* <<start-dfanalytics,Start {dfanalytics-jobs}>>
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* <<stop-dfanalytics,Stop {dfanalytics-jobs}>>
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* <<evaluate-dfanalytics,Evaluate {dfanalytics}>>
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* <<explain-dfanalytics,Explain {dfanalytics}>>
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You can use the following APIs to perform {infer} operations.
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* <<put-trained-models>>
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* <<get-trained-models>>
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* <<get-trained-models-stats>>
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* <<delete-trained-models>>
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* <<put-trained-models-aliases>>
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* <<delete-trained-models-aliases>>
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You can deploy a trained model to make predictions in an ingest pipeline or in
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an aggregation. Refer to the following documentation to learn more.
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* <<inference-processor,{infer-cap} processor>>
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* <<search-aggregations-pipeline-inference-bucket-aggregation,{infer-cap} bucket aggregation>>
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See also <<ml-apis>>.
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