1. Secure Delivery of AI Applications with AWS AppStream


    AWS AppStream 2.0 is a fully managed application streaming service that provides users with instant access to their desktop applications from anywhere by streaming them over the web. This service is ideal for delivering AI applications securely since it runs in an AWS environment and streams only the application UI to end-users, with no data ever being stored on their devices.

    To set up a secure delivery of AI applications using AWS AppStream, you will follow these general steps:

    1. Configure an AppStream 2.0 fleet, which is a pool of streaming instances that will run your applications.
    2. Create an AppStream 2.0 stack, which defines the set of applications and settings the user can access.
    3. Associate the fleet with the stack.
    4. Assign users to the stack so they can access the applications.

    Below is an example Pulumi program in Python that sets up an AWS AppStream environment for secure delivery of AI applications. The program includes resources like a fleet, stack, and their association with the necessary configurations using the pulumi_aws package.

    import pulumi import pulumi_aws as aws # Create an AppStream image builder to create custom images # For AI applications, you would use an image with relevant software pre-installed. image_builder = aws.appstream.ImageBuilder("aiImageBuilder", name="ai-image-builder", display_name="AI Image Builder", instance_type="stream.standard.medium", # Choose an appropriate instance type for your application image_name="AppStream-WinServer2016-06-01-2021", # This should be the name of a preexisting AppStream image iam_role_arn="<IAM_ROLE_ARN>", # Specify the ARN of the IAM role that has AppStream permissions # More configurations can be added such as VPC, domain join info, tags, etc. ) # Create an AppStream fleet # Fleets consist of streaming instances that will run your applications fleet = aws.appstream.Fleet("aiApplicationFleet", name="ai-application-fleet", instance_type="stream.standard.medium", # Choose the instance type based on the needs of your AI applications image_arn=image_builder.image_arn, # Use the ARN of the custom image built by your image builder fleet_type="ON_DEMAND", # 'ALWAYS_ON' or 'ON_DEMAND' based on how you want to manage capacity # Include further configurations for VPC, IAM roles, max user duration, disconnect timeout settings, etc. ) # Create an AppStream stack # Stacks hold settings and configurations for your end user's sessions stack = aws.appstream.Stack("aiApplicationStack", name="ai-application-stack", description="Stack for AI Applications", display_name="AI Application Stack", # Define user settings such as access policies, embed host domains, storage connectors, etc. ) # Associate the fleet with the stack # This enables the stack to use instances from the specified fleet to stream applications fleet_stack_association = aws.appstream.FleetStackAssociation("aiFleetStackAssoc", fleet_name=fleet.name, stack_name=stack.name, ) # Export the stack and fleet details for access and further configuration pulumi.export("stack_name", stack.name) pulumi.export("fleet_name", fleet.name)

    In the above program:

    • We start by creating an ImageBuilder resource, which is necessary to create custom images tailored for AI applications. You would customize this image with software and dependencies your AI application requires.
    • Then we define a Fleet, which is a pool of EC2 instances optimized for AppStream. This will run the AI applications.
    • A Stack is defined to manage application access policies and provide a consistent set of resources for user sessions.
    • We associate the fleet with the stack through a FleetStackAssociation, which effectively pairs the fleet instances with the stack configuration, so end-users can access applications within the fleet.

    The ARNs and specific names used here are placeholders, and you would replace them with your actual resource names and ARNs. Ensure that the IAM role has the appropriate permissions to manage AppStream resources and access other AWS services as needed for your AI application.

    After setting up these resources, you would proceed to configure user access and application entitlements, which can also be managed through additional Pulumi resources such as aws.appstream.User and aws.appstream.Application.

    Please note that the particulars of your AI application—such as computational requirements, necessary software, and AWS resources—will influence the exact configurations of these resources.