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a68c6acdb3
This commit removes the ability to set the vocabulary location in the model config. This opts instead for sane defaults to be set and used. Wrapping this up in an API. The index is now always the internally managed .ml-inference-native index and the document ID is always <model_id>_vocabulary This API only works for pytorch/nlp type models.
40 lines
1.4 KiB
Text
40 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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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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