elasticsearch/docs/reference/ml/df-analytics/apis/ml-df-analytics-apis.asciidoc

[role="xpack"]
[testenv="platinum"]
[[ml-df-analytics-apis]]
= {ml-cap} {dfanalytics} APIs

You can use the following APIs to perform {ml} {dfanalytics} activities:

* <<put-dfanalytics,Create {dfanalytics-jobs}>>
* <<delete-dfanalytics,Delete {dfanalytics-jobs}>>
* <<get-dfanalytics,Get {dfanalytics-jobs} info>>
* <<get-dfanalytics-stats,Get {dfanalytics-jobs} statistics>>
* <<evaluate-dfanalytics,Evaluate {dfanalytics}>>
* <<explain-dfanalytics,Explain {dfanalytics}>>
* <<preview-dfanalytics,Preview {dfanalytics}>>
* <<start-dfanalytics,Start {dfanalytics-jobs}>>
* <<stop-dfanalytics,Stop {dfanalytics-jobs}>>
* <<update-dfanalytics,Update {dfanalytics-jobs}>>

You can use the following APIs to perform {infer} operations:

* <<put-trained-models>>
* <<put-trained-models-aliases>>
* <<delete-trained-models>>
* <<delete-trained-models-aliases>>
* <<get-trained-models>>
* <<get-trained-models-stats>>

You can deploy a trained model to make predictions in an ingest pipeline or in
an aggregation. Refer to the following documentation to learn more:

* <<search-aggregations-pipeline-inference-bucket-aggregation,{infer-cap} bucket aggregation>>
* <<inference-processor,{infer-cap} processor>>
* <<infer-trained-model-deployment>>
* <<start-trained-model-deployment>>
* <<stop-trained-model-deployment>>


See also <<ml-apis>>.