1. Point-in-Time Restore Capabilities for AI Model Artifacts.


    Point-in-time restore is a feature that allows you to go back to a specific moment in time, recovering data from that exact state. For artificial intelligence (AI) model artifacts, this can be crucial, as you often need to revert to a previous iteration of your model if new changes have introduced errors or decreased performance.

    In the context of cloud infrastructure and AI model workflows, point-in-time restore can be enabled using cloud services that support versioning and snapshots of your data. For AI models, artifacts are typically stored in object storage services or specialized artifact repositories that support versioning. Pulumi provides an infrastructure as code approach to manage these services efficiently.

    For the purpose of this explanation, we will focus on AWS as the cloud provider, though similar capabilities exist across Azure, GCP, and other cloud providers.

    AWS provides versioning capabilities through services like Amazon S3 (Simple Storage Service), where you can keep multiple versions of an object in the same bucket. When you enable versioning on an S3 bucket, each object's version is uniquely identified by a version ID. Additionally, with AWS Backup, you can protect S3 buckets by automating backup policies and restoring previous versions when needed.

    Below is a Pulumi Python program that demonstrates how to:

    1. Create an Amazon S3 bucket with versioning enabled, which will store your AI model artifacts.
    2. Define an AWS Backup plan to automate the creation of recovery points.

    Let's walk through the code:

    import pulumi import pulumi_aws as aws # Create an S3 bucket to store the AI Model artifacts. ai_model_artifacts_bucket = aws.s3.Bucket("aiModelArtifactsBucket", acl="private", versioning=aws.s3.BucketVersioningArgs( enabled=True, ) ) # AWS Backup vault to manage backups of the S3 bucket. backup_vault = aws.backup.Vault("backupVault", { "recovery_points_vault_name": "ai-model-artifacts-vault" }) # AWS Backup plan to define the backup rules and assign resources. backup_plan = aws.backup.Plan("backupPlan", { "name": "ai-model-artifacts-backup-plan", "rules": [{ "rule_name": "Daily", "target_vault_name": backup_vault.name, "schedule": "cron(0 12 * * ? *)", # Run daily at 12:00 UTC. "start_window": 120, # Minutes before transition to cold storage. "completion_window": 360, # Minutes before backup job completion. "lifecycle": { "cold_storage_after": 30, # Days before moving to cold storage. "delete_after": 90, # Days before deleting the recovery point. }, }], }) # Assigning the S3 bucket to the backup plan using a backup selection. backup_selection = aws.backup.Selection("backupSelection", { "iam_role_arn": "arn:aws:iam::123456789012:role/aws-service-role/backup.amazonaws.com/AWSServiceRoleForBackup", "plan_id": backup_plan.id, "resources": [ ai_model_artifacts_bucket.arn, # ARN of the S3 bucket. ], }) # Export the S3 bucket name pulumi.export("ai_model_artifacts_bucket_name", ai_model_artifacts_bucket.id)

    In this program, we define an S3 bucket with versioning enabled, meaning that each update or delete operation will preserve the previous state of the objects stored within. This already provides a level of point-in-time restore functionality by allowing you to retrieve preceding versions of your AI model artifacts.

    To enhance our point-in-time restore capability, we employ AWS Backup to create a vault where our backups will be stored and to define a backup plan. This plan schedules backups, sets a window of time to perform them, and defines the lifecycle of the recovery points, including moving to cold storage and deletion policies.

    Please replace "arn:aws:iam::123456789012:role/aws-service-role/backup.amazonaws.com/AWSServiceRoleForBackup" with your actual IAM role ARN that has the necessary permissions to perform backup operations.

    We then create a backup selection, which connects our S3 bucket to the backup plan using the bucket's ARN.

    By exporting the bucket's name at the end of the program, you can easily identify the bucket where your AI model artifacts are being stored.

    To use the above program:

    • Ensure you have installed Pulumi and configured AWS credentials.
    • Save this code in a file named __main__.py.
    • Run pulumi up in the same directory as your program file to deploy the resources.