UpdateMLModel
Updates the MLModelName
and the ScoreThreshold
of an MLModel
.
You can use the GetMLModel
operation to view the contents of the updated data element.
Request Syntax
{
"MLModelId": "string
",
"MLModelName": "string
",
"ScoreThreshold": number
}
Request Parameters
For information about the parameters that are common to all actions, see Common Parameters.
The request accepts the following data in JSON format.
- MLModelId
-
The ID assigned to the
MLModel
during creation.Type: String
Length Constraints: Minimum length of 1. Maximum length of 64.
Pattern:
[a-zA-Z0-9_.-]+
Required: Yes
- MLModelName
-
A user-supplied name or description of the
MLModel
.Type: String
Length Constraints: Maximum length of 1024.
Pattern:
.*\S.*|^$
Required: No
- ScoreThreshold
-
The
ScoreThreshold
used in binary classificationMLModel
that marks the boundary between a positive prediction and a negative prediction.Output values greater than or equal to the
ScoreThreshold
receive a positive result from theMLModel
, such astrue
. Output values less than theScoreThreshold
receive a negative response from theMLModel
, such asfalse
.Type: Float
Required: No
Response Syntax
{
"MLModelId": "string"
}
Response Elements
If the action is successful, the service sends back an HTTP 200 response.
The following data is returned in JSON format by the service.
- MLModelId
-
The ID assigned to the
MLModel
during creation. This value should be identical to the value of theMLModelID
in the request.Type: String
Length Constraints: Minimum length of 1. Maximum length of 64.
Pattern:
[a-zA-Z0-9_.-]+
Errors
For information about the errors that are common to all actions, see Common Errors.
- InternalServerException
-
An error on the server occurred when trying to process a request.
HTTP Status Code: 500
- InvalidInputException
-
An error on the client occurred. Typically, the cause is an invalid input value.
HTTP Status Code: 400
- ResourceNotFoundException
-
A specified resource cannot be located.
HTTP Status Code: 400
Examples
The following is a sample request and response of the UpdateMLModel operation.
This example illustrates one usage of UpdateMLModel.
Sample Request
POST / HTTP/1.1
Host: machinelearning.<region>.<domain>
x-amz-Date: <Date>
Authorization: AWS4-HMAC-SHA256 Credential=<Credential>, SignedHeaders=contenttype;date;host;user-agent;x-amz-date;x-amz-target;x-amzn-requestid,Signature=<Signature>
User-Agent: <UserAgentString>
Content-Type: application/x-amz-json-1.1
Content-Length: <PayloadSizeBytes>
Connection: Keep-Alive
X-Amz-Target: AmazonML_20141212.UpdateMLModel
{
"MLModelId": "ml-exampleModelId",
"MLModelName": "ml-exampleModelName",
"ScoreThreshold": 0.8
}
Sample Response
HTTP/1.1 200 OK
x-amzn-RequestId: <RequestId>
Content-Type: application/x-amz-json-1.1
Content-Length: <PayloadSizeBytes>
Date: <Date>
{"MLModelId": "pr-exampleModelId"}
See Also
For more information about using this API in one of the language-specific AWS SDKs, see the following: