Scalable Serving of Machine Learning Models

Question

Pulumi · Accepted Answer

To serve machine learning models in a scalable manner, you typically need a platform that can host your trained models and expose them through a reliable and scalable endpoint. Cloud platforms like AWS, Azure, and Google Cloud offer services that facilitate this, and by using Pulumi, you can define, deploy, and manage these services as infrastructure as code.

For this purpose, we'll focus on deploying a machine learning model using AWS SageMaker, which is a fully managed service that provides the ability to build, train, and deploy machine learning models quickly.

AWS SageMaker has a concept of 'Model' which represents the artifacts of a trained machine learning model. A 'ModelPackageGroup' is a higher-level construct that groups together SageMaker Model Packages, which can encapsulate different versions of the models you want to serve.

To serve predictions, we deploy a 'Model' to a 'SageMaker Endpoint', a scalable and managed solution that hosts your model and responds to inference requests in real-time. The 'Endpoint' automatically scales the compute resources in response to the volume of inference requests, ensuring high availability.

In the Pulumi program below, we will:

1. Create a SageMaker Model Package Group to manage different versions of our model packages.
2. Define a SageMaker Model, which references the location of our trained model artifacts.
3. Deploy the model to a SageMaker Endpoint for real-time inference.

```python
import pulumi
import pulumi_aws_native as aws_native

# Create a SageMaker ModelPackageGroup
model_package_group = aws_native.sagemaker.ModelPackageGroup("myModelPackageGroup",
    model_package_group_name="my-model-package-group",
    model_package_group_description="Group for my ML models"
)

# Define the location of the trained model artifacts in S3
model_data_url = "s3://my-model-bucket/model.tar.gz"

# Define the SageMaker Model, based on the model artifacts
model = aws_native.sagemaker.Model("myModel",
    model_name="my-model",
    execution_role_arn="arn:aws:iam::123456789012:role/SageMakerExecutionRole",  # Replace with your SageMaker execution role ARN
    primary_container=aws_native.sagemaker.ModelPrimaryContainerArgs(
        image="246618743249.dkr.ecr.us-west-2.amazonaws.com/sagemaker-tensorflow-serving:2.3", # Replace with your choice of inference image
        model_data_url=model_data_url
    )
)

# Create a SageMaker Endpoint Configuration
endpoint_config = aws_native.sagemaker.EndpointConfig("myEndpointConfig",
    endpoint_config_name="my-endpoint-config",
    production_variants=[aws_native.sagemaker.EndpointConfigProductionVariantsArgs(
        variant_name="AllTraffic",
        model_name=model.name,
        initial_instance_count=1,
        instance_type="ml.m5.large"
    )]
)

# Deploy the SageMaker Model to an Endpoint
endpoint = aws_native.sagemaker.Endpoint("myEndpoint",
    endpoint_name="my-endpoint",
    endpoint_config_name=endpoint_config.name
)

# Export the SageMaker Endpoint name
pulumi.export("sagemaker_endpoint_name", endpoint.endpoint_name)
```

In this example, we start by defining the necessary resources for serving our machine learning model. The `ModelPackageGroup` is a logical grouping for our model artifacts. We then specify the location of our trained model artifacts stored in an S3 bucket as `model_data_url`.

We create the SageMaker `Model` specifying an execution role and the Docker image that can serve our model. The execution role should have permissions to access necessary AWS resources and perform operations on behalf of the SageMaker service.

The `EndpointConfig` defines the deployment characteristics like the instance type and count. Finally, we create the SageMaker `Endpoint` using this configuration, which will serve our model.

The `pulumi.export` statement will output the name of the Endpoint, which you can use to interface with the deployed model for inference.

Please ensure that you replace the placeholders like execution role ARN, S3 model path, and Docker image URI with your actual configurations.

Once you run the Pulumi program, it will create these resources in your AWS account. You can manage your infrastructure using Pulumi's CLI to apply updates, preview changes, and clean up resources when no longer needed.