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* Refactor ML section - local and remote models Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Added command to calculate checksum Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add ONNX format to register API Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add sparse encoding predict example Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add API section Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Refactor the API section Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Typo Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Implemented Vale comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add get connector API Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Reword heading Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Addressed tech review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --------- Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Co-authored-by: Melissa Vagi <vagimeli@amazon.com>
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layout | title | parent | grand_parent | has_children | nav_order |
---|---|---|---|---|---|
default | Predict | Train and Predict APIs | ML Commons API | true | 20 |
Predict
ML Commons can predict new data with your trained model either from indexed data or a data frame. To use the Predict API, the model_id
is required.
For information about user access for this API, see Model access control considerations.
Path and HTTP methods
POST /_plugins/_ml/_predict/<algorithm_name>/<model_id>
Example request
POST /_plugins/_ml/_predict/kmeans/<model-id>
{
"input_query": {
"_source": ["petal_length_in_cm", "petal_width_in_cm"],
"size": 10000
},
"input_index": [
"iris_data"
]
}
{% include copy-curl.html %}
Example response
{
"status" : "COMPLETED",
"prediction_result" : {
"column_metas" : [
{
"name" : "ClusterID",
"column_type" : "INTEGER"
}
],
"rows" : [
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 1
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 1
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
},
{
"values" : [
{
"column_type" : "INTEGER",
"value" : 0
}
]
}
]
}
}