async fn create_endpoint_config(
        &self,
        input: CreateEndpointConfigInput,
    ) -> Result<CreateEndpointConfigOutput, RusotoError<CreateEndpointConfigError>> {
        let mut request = self.new_signed_request("POST", "/");
        request.add_header("x-amz-target", "SageMaker.CreateEndpointConfig");
        let encoded = serde_json::to_string(&input).unwrap();
        request.set_payload(Some(encoded));

        let response = self
            .sign_and_dispatch(request, CreateEndpointConfigError::from_response)
            .await?;
        let mut response = response;
        let response = response.buffer().await.map_err(RusotoError::HttpDispatch)?;
        proto::json::ResponsePayload::new(&response).deserialize::<CreateEndpointConfigOutput, _>()
    }
<p><p>Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the <code>CreateModel</code> API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the <a>CreateEndpoint</a> API.</p> <note> <p> Use this API if you want to use Amazon SageMaker hosting services to deploy models into production. </p> </note> <p>In the request, you define a <code>ProductionVariant</code>, for each model that you want to deploy. Each <code>ProductionVariant</code> parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy. </p> <p>If you are hosting multiple models, you also assign a <code>VariantWeight</code> to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B. </p> <p>For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-deploy-model.html#ex1-deploy-model-boto">Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).</a> </p> <note> <p>When you call <a>CreateEndpoint</a>, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting <a href="https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/HowItWorks.ReadConsistency.html"> <code>Eventually Consistent Reads</code> </a>, the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call <a>DescribeEndpointConfig</a> before calling <a>CreateEndpoint</a> to minimize the potential impact of a DynamoDB eventually consistent read.</p> </note></p>
rusoto/services/sagemaker/src/generated.rs