http://lookoutvision.{region}.amazonaws.com/2020-11-20/projects/{projectName}/models<p>Creates a new version of a model within an an Amazon Lookout for Vision project. <code>CreateModel</code> is an asynchronous operation in which Amazon Lookout for Vision trains, tests, and evaluates a new version of a model. </p> <p>To get the current status, check the <code>Status</code> field returned in the response from <a>DescribeModel</a>.</p> <p>If the project has a single dataset, Amazon Lookout for Vision internally splits the dataset to create a training and a test dataset. If the project has a training and a test dataset, Lookout for Vision uses the respective datasets to train and test the model. </p> <p>After training completes, the evaluation metrics are stored at the location specified in <code>OutputConfig</code>. </p> <p>This operation requires permissions to perform the <code>lookoutvision:CreateModel</code> operation. If you want to tag your model, you also require permission to the <code>lookoutvision:TagResource</code> operation.</p>
The name of the project in which you want to create a model version.
A set of tags (key-value pairs) that you want to attach to the model.
The identifier for your AWS KMS key. The key is used to encrypt training and test images copied into the service for model training. Your source images are unaffected. If this parameter is not specified, the copied images are encrypted by a key that AWS owns and manages.
A description for the version of the model.
The S3 location where Amazon Lookout for Vision saves model training files.
{
"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://lookoutvision.{region}.amazonaws.com/2020-11-20/projects/{projectName}/models' \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://lookoutvision.{region}.amazonaws.com/2020-11-20/projects/{projectName}/models<p>Creates a new version of a model within an an Amazon Lookout for Vision project. <code>CreateModel</code> is an asynchronous operation in which Amazon Lookout for Vision trains, tests, and evaluates a new version of a model. </p> <p>To get the current status, check the <code>Status</code> field returned in the response from <a>DescribeModel</a>.</p> <p>If the project has a single dataset, Amazon Lookout for Vision internally splits the dataset to create a training and a test dataset. If the project has a training and a test dataset, Lookout for Vision uses the respective datasets to train and test the model. </p> <p>After training completes, the evaluation metrics are stored at the location specified in <code>OutputConfig</code>. </p> <p>This operation requires permissions to perform the <code>lookoutvision:CreateModel</code> operation. If you want to tag your model, you also require permission to the <code>lookoutvision:TagResource</code> operation.</p>
The name of the project in which you want to create a model version.
A set of tags (key-value pairs) that you want to attach to the model.
The identifier for your AWS KMS key. The key is used to encrypt training and test images copied into the service for model training. Your source images are unaffected. If this parameter is not specified, the copied images are encrypted by a key that AWS owns and manages.
A description for the version of the model.
The S3 location where Amazon Lookout for Vision saves model training files.
{
"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://lookoutvision.{region}.amazonaws.com/2020-11-20/projects/{projectName}/models' \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}