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InferenceComponent

Learn how to create, update, and manage AWS SageMaker InferenceComponents using Alchemy Cloud Control.

The InferenceComponent resource allows you to manage AWS SageMaker InferenceComponents for deploying machine learning models and serving predictions.

Create a basic InferenceComponent with required properties and one optional property:

import AWS from "alchemy/aws/control";
const inferenceComponent = await AWS.SageMaker.InferenceComponent("basicInferenceComponent", {
EndpointName: "my-endpoint",
Specification: {
ContentType: "application/json",
InputPath: "/input",
OutputPath: "/output"
},
VariantName: "AllTraffic" // Common optional property
});

Configure an InferenceComponent with additional options such as runtime configuration and deployment settings:

const advancedInferenceComponent = await AWS.SageMaker.InferenceComponent("advancedInferenceComponent", {
EndpointName: "my-endpoint",
Specification: {
ContentType: "application/json",
InputPath: "/input",
OutputPath: "/output"
},
RuntimeConfig: {
Environment: {
LOG_LEVEL: "DEBUG"
}
},
DeploymentConfig: {
AutoRollbackConfiguration: {
Alarms: [
{
AlarmName: "HighErrorRate",
AlarmType: "CLOUDWATCH",
Interval: 60
}
]
}
}
});

Create an InferenceComponent and assign tags for better resource management:

const taggedInferenceComponent = await AWS.SageMaker.InferenceComponent("taggedInferenceComponent", {
EndpointName: "my-endpoint",
Specification: {
ContentType: "application/json",
InputPath: "/input",
OutputPath: "/output"
},
Tags: [
{ Key: "Project", Value: "ImageRecognition" },
{ Key: "Environment", Value: "Production" }
]
});

If you want to adopt an existing InferenceComponent instead of creating a new one, you can set the adopt property to true:

const adoptedInferenceComponent = await AWS.SageMaker.InferenceComponent("adoptedInferenceComponent", {
EndpointName: "existing-endpoint",
Specification: {
ContentType: "application/json",
InputPath: "/input",
OutputPath: "/output"
},
adopt: true // Adopt existing resource
});