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Removes `testenv` annotations and related code. These annotations originally let you skip x-pack snippet tests in the docs. However, that's no longer possible. Relates to #79309, #31619 # Conflicts: # docs/reference/ml/df-analytics/apis/get-trained-model-deployment-stats.asciidoc # docs/reference/ml/df-analytics/apis/infer-trained-model-deployment.asciidoc # docs/reference/ml/df-analytics/apis/put-trained-model-definition-part.asciidoc # docs/reference/ml/df-analytics/apis/put-trained-model-vocabulary.asciidoc # docs/reference/ml/df-analytics/apis/start-trained-model-deployment.asciidoc # docs/reference/ml/df-analytics/apis/stop-trained-model-deployment.asciidoc # docs/reference/slm/apis/slm-delete.asciidoc # docs/reference/slm/apis/slm-execute-retention.asciidoc # docs/reference/slm/apis/slm-execute.asciidoc # docs/reference/slm/apis/slm-get-status.asciidoc # docs/reference/slm/apis/slm-get.asciidoc # docs/reference/slm/apis/slm-start.asciidoc # docs/reference/slm/apis/slm-stats.asciidoc # docs/reference/slm/apis/slm-stop.asciidoc # docs/reference/sql/endpoints/client-apps/tableau-desktop.asciidoc # docs/reference/sql/endpoints/client-apps/tableau-server.asciidoc
35 lines
1.2 KiB
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35 lines
1.2 KiB
Text
[role="xpack"]
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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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