1. Global AI Model Deployment with Akamai CDN


    To deploy a global AI model with Akamai CDN, you'll need to have a machine learning model ready for deployment and a CDN configuration to distribute the model's API. Below is an explanation followed by a Pulumi program written in Python that sets up the necessary infrastructure on Google Cloud Platform (GCP) for the AI model, along with an Akamai CDN setup to distribute access to the model globally.

    Here are the key components of the setup:

    1. Google Cloud AI Platform Model (EngineModel): This component of the Google Cloud Platform allows you to host your machine learning model. It manages the infrastructure needed to run predictions and is scalable and secure. Here, you will need an already trained model that you want to deploy.

    2. Akamai CDN (CpCode): Akamai's Content Delivery Network (CDN) will be used to distribute and accelerate access to your machine learning model's API endpoint. The CpCode resource in Akamai is a unique identifier that is used to track and control the delivery of content on Akamai's network.

    The following Pulumi program creates a new Google Cloud AI Engine model ready for prediction serving and establishes a CP Code in Akamai to distribute API access.

    import pulumi import pulumi_gcp as gcp import pulumi_akamai as akamai # Assuming the user has Google Cloud project and Akamai details configured. # Create a Google Cloud AI Engine Model # For this to work, you'll need to have a trained model ready for deployment. ai_model = gcp.ml.EngineModel("aiModel", name="global-ai-model", description="A global AI model for predictions", project="your-gcp-project-id", regions=["global"]) # Create a CP Code with Akamai to use with the model's API endpoint distribution cp_code = akamai.CpCode("cpCode", name="global-ai-model-cpcode", contractId="your-akamai-contract-id", groupId="your-akamai-group-id", productId="prd_SPM") # Assuming 'SPM' (Secure Performance Management) product usage. # Output the details pulumi.export('ai_model_name', ai_model.name) pulumi.export('cp_code_name', cp_code.name)

    Let's explain each part of the code:

    1. We import pulumi, pulumi_gcp, and pulumi_akamai, which are the official Pulumi modules to interact with resources on Google Cloud and Akamai.

    2. Next, we create an instance of EngineModel, which represents our AI model on Google Cloud. We provide details such as the model's name, description, associated project ID, and the regions where the model is available.

    3. We also create a CpCode in Akamai, providing a name, contract ID, group ID, and product ID. These values should be specific to your Akamai account and establish a unique identifier for your model's API endpoint within the Akamai CDN.

    4. Finally, we export the names of the created resources. In a production environment, you might also want to export endpoints or IDs that would be used to connect the Google Cloud AI model API with the Akamai CDN.

    Please ensure you replace your-gcp-project-id, your-akamai-contract-id, your-akamai-group-id, and potentially prd_SPM with the actual identifiers from your Google Cloud and Akamai accounts.

    This program assumes you have already set up Pulumi with the necessary access to Google Cloud and Akamai. Additionally, it assumes that the machine learning model is already trained and ready for deployment.

    Keep in mind that this is a basic setup and a starting point. Depending on your specific requirements, you might need additional configurations for things like access control, logging, monitoring, and specific machine learning model configurations on Google Cloud AI Platform.