1. AI Model Inference Input/Output Storage with Spaces


    To create an infrastructure for AI model inference that involves input and output storage using Pulumi, we would typically want to provision cloud storage resources where the AI models, input data, and inference results can be stored. For this demonstration, we'll use AWS as the cloud provider, and we will create a SageMaker Space and an S3 bucket. AWS SageMaker provides a fully managed machine learning service called SageMaker Spaces that can be used to host your machine learning models, while S3 buckets can be used to store input data and model inference outputs.

    Here's a step-by-step explanation of what we're going to do in the Pulumi program below:

    1. Set up an AWS S3 bucket where you'll store input data and inference results. S3 is a highly durable and available storage service that is ideal for this use case.
    2. Create an AWS SageMaker Space with the necessary configurations. This will be the environment where you'll deploy your AI models for inference. You need to have a domain ID and space name which are specific to your AWS account and SageMaker configuration.

    For the sake of simplicity and illustration, I’m not covering the model deployment steps within SageMaker Space, as that can be a complex process and varies depending on the actual model and framework being used.

    Now, let's write the Pulumi program to create these resources:

    import pulumi import pulumi_aws as aws # Create an S3 bucket to store your AI model's input and output data. ai_data_bucket = aws.s3.Bucket("aiDataBucket", acl="private", tags={ "Name": "AI Model Data Bucket", }) # Create a SageMaker Space to host your AI models. # Note: You will have to replace `your_domain_id` with your actual SageMaker domain ID. sagemaker_space = aws.sagemaker.Space("aiModelSpace", domain_id="your_domain_id", # Replace with your SageMaker domain ID space_name="my-ai-model-space", space_settings=aws.sagemaker.SpaceSpaceSettingsArgs( jupyter_server_app_settings=aws.sagemaker.SpaceJupyterServerAppSettingsArgs( default_resource_spec=aws.sagemaker.SpaceDefaultResourceSpecArgs( instance_type="ml.t2.medium", # Choose your desired instance type ) ) )) # Export the S3 bucket name and SageMaker space ARN as stack outputs pulumi.export("s3_bucket_name", ai_data_bucket.id) pulumi.export("sagemaker_space_arn", sagemaker_space.arn)

    This Pulumi program will provision a private S3 bucket and a SageMaker Space that you can later configure to deploy your AI models. Replace the your_domain_id placeholder with your actual SageMaker domain ID when running this code.

    After deploying these resources, data scientists or ML engineers can manage the lifecycles of input/output datasets in S3 and deploy models for inference in the configured SageMaker Space.

    Remember that this is a basic setup and doesn't handle access policies or data processing workflows, which are usually necessary for production environments. You'd also need to handle authentication and authorization, network security, and logging and monitoring according to your organization's requirements.