Endpoint
The Endpoint resource lets you manage AWS SageMaker Endpoints for deploying machine learning models. Endpoints provide a way to host your model for real-time inference, allowing applications to make predictions based on input data.
Minimal Example
Section titled “Minimal Example”Create a basic SageMaker Endpoint with required properties and some common optional configurations.
import AWS from "alchemy/aws/control";
const sageMakerEndpoint = await AWS.SageMaker.Endpoint("mySageMakerEndpoint", { EndpointConfigName: "myEndpointConfig", RetainAllVariantProperties: true});
Advanced Configuration
Section titled “Advanced Configuration”Configure a SageMaker Endpoint with a deployment configuration and tags for better management.
const advancedSageMakerEndpoint = await AWS.SageMaker.Endpoint("advancedSageMakerEndpoint", { EndpointConfigName: "myAdvancedEndpointConfig", DeploymentConfig: { AutoRollbackConfiguration: { Alarms: [ { AlarmName: "EndpointErrorAlarm", AlarmType: "ERROR" } ] } }, Tags: [ { Key: "Environment", Value: "Production" }, { Key: "Project", Value: "AIModelDeployment" } ]});
Excluding Variant Properties
Section titled “Excluding Variant Properties”Create a SageMaker Endpoint while excluding specific variant properties from the deployment.
const variantExclusionEndpoint = await AWS.SageMaker.Endpoint("variantExclusionEndpoint", { EndpointConfigName: "myVariantExclusionConfig", ExcludeRetainedVariantProperties: [ { VariantName: "LowPriorityVariant" } ], RetainDeploymentConfig: false});
Adoption of Existing Resource
Section titled “Adoption of Existing Resource”Adopt an existing SageMaker Endpoint if it already exists, preventing failure due to duplication.
const adoptExistingEndpoint = await AWS.SageMaker.Endpoint("adoptExistingEndpoint", { EndpointConfigName: "myExistingEndpointConfig", adopt: true});