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* [ML] add documentation for get deployment stats API * Apply suggestions from code review Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co> Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
41 lines
1.4 KiB
Text
41 lines
1.4 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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* <<put-dfanalytics,Create {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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* <<evaluate-dfanalytics,Evaluate {dfanalytics}>>
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* <<explain-dfanalytics,Explain {dfanalytics}>>
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* <<preview-dfanalytics,Preview {dfanalytics}>>
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* <<start-dfanalytics,Start {dfanalytics-jobs}>>
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* <<stop-dfanalytics,Stop {dfanalytics-jobs}>>
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* <<update-dfanalytics,Update {dfanalytics-jobs}>>
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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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* <<put-trained-model-definition-part>>
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* <<put-trained-model-vocabulary>>
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* <<put-trained-models-aliases>>
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* <<delete-trained-models>>
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* <<delete-trained-models-aliases>>
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* <<get-trained-models>>
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* <<get-trained-models-stats>>
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* <<get-trained-model-deployment-stats>>
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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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* <<search-aggregations-pipeline-inference-bucket-aggregation,{infer-cap} bucket aggregation>>
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* <<inference-processor,{infer-cap} processor>>
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* <<infer-trained-model-deployment>>
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* <<start-trained-model-deployment>>
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* <<stop-trained-model-deployment>>
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See also <<ml-apis>>.
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