http://glue.{region}.amazonaws.com/#X-Amz-Target=AWSGlue.StartMLEvaluationTaskRun<p>Starts a task to estimate the quality of the transform. </p> <p>When you provide label sets as examples of truth, Glue machine learning uses some of those examples to learn from them. The rest of the labels are used as a test to estimate quality.</p> <p>Returns a unique identifier for the run. You can call <code>GetMLTaskRun</code> to get more information about the stats of the <code>EvaluationTaskRun</code>.</p>
{
"success": true,
"data": {
"id": "abc123",
"created_at": "2025-01-01T00:00:00Z"
}
}{
"success": false,
"error": {
"code": "VALIDATION_ERROR",
"message": "Invalid request parameters"
}
}1curl --request POST \2 --url 'http://glue.{region}.amazonaws.com/#X-Amz-Target=AWSGlue.StartMLEvaluationTaskRun' \3 --header 'accept: application/json' \4 --header 'content-type: application/json'1{2 "success": true,3 "data": {4 "id": "abc123",5 "created_at": "2025-01-01T00:00:00Z"6 }7}http://glue.{region}.amazonaws.com/#X-Amz-Target=AWSGlue.StartMLEvaluationTaskRun<p>Starts a task to estimate the quality of the transform. </p> <p>When you provide label sets as examples of truth, Glue machine learning uses some of those examples to learn from them. The rest of the labels are used as a test to estimate quality.</p> <p>Returns a unique identifier for the run. You can call <code>GetMLTaskRun</code> to get more information about the stats of the <code>EvaluationTaskRun</code>.</p>
{
"success": true,
"data": {
"id": "abc123",
"created_at": "2025-01-01T00:00:00Z"
}
}{
"success": false,
"error": {
"code": "VALIDATION_ERROR",
"message": "Invalid request parameters"
}
}1curl --request POST \2 --url 'http://glue.{region}.amazonaws.com/#X-Amz-Target=AWSGlue.StartMLEvaluationTaskRun' \3 --header 'accept: application/json' \4 --header 'content-type: application/json'1{2 "success": true,3 "data": {4 "id": "abc123",5 "created_at": "2025-01-01T00:00:00Z"6 }7}