1. Isolation of AI Environments with MongoDB Atlas Projects


    Isolating AI environments in MongoDB Atlas can be beneficial for managing different environments, such as development, testing, and production. Separation of these environments ensures that your teams can work independently on their tasks without interfering with each other, maintaining data integrity and security.

    To achieve isolation with MongoDB Atlas, you typically create separate projects for each environment. A MongoDB Atlas project acts as an independent workspace containing your MongoDB clusters, databases, users, and more. By separating these into different projects, you not only achieve organizational clarity but also set the stage for fine-grained access control and resource management.

    When working with Pulumi to create MongoDB Atlas resources, like projects or clusters, we use the pulumi_mongodbatlas package. This package provides all the necessary resource types to interact with MongoDB Atlas services.

    Here, I'll demonstrate how to create a new project in MongoDB Atlas, which is the first step towards creating isolated AI environments. Please note that for this code to work, you should have the MongoDB Atlas provider configured with your Atlas public and private keys.

    Below is a Pulumi program that creates a new project in MongoDB Atlas:

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Create a new MongoDB Atlas Project. # This project will act as an isolated environment. ai_project = mongodbatlas.Project("aiProject", # The name of the MongoDB Atlas Project - you can customize it to reflect the type of environment, like "AI-Development" / "AI-Production" name="AI-Environment", # Organization ID under which you want to create a project. org_id="YOUR_ORG_ID", # The following setting enrolls the created project into the default alert settings template. with_default_alerts_settings=True) # The project ID is a useful output that you might use to manage other resources in MongoDB Atlas. pulumi.export("project_id", ai_project.id) # The below would export the name of the project, which you can use for referencing in policies or in your applications. pulumi.export("project_name", ai_project.name)

    In this Pulumi program:

    • We import pulumi and pulumi_mongodbatlas.
    • We create a new MongoDB Atlas Project with the name AI-Environment.
    • We add the org_id, which is your MongoDB Atlas organization ID where you want the new project to reside.
    • We set with_default_alerts_settings to True to automatically enroll the new project into the default alert settings provided by MongoDB Atlas.
    • Lastly, we export the project ID and name. These outputs can be used for further automation or for reference purposes.

    This program is a starting point. After the project is created, you can proceed to create clusters within this project, configure databases, manage users, and establish network settings like VPC peering or IP whitelisting, all using the Pulumi MongoDB Atlas provider. Each of these resources would have their corresponding types and configurations similar to the one shown in the example above.

    To run this Pulumi program, save it as __main__.py inside a new Pulumi project directory and run pulumi up after replacing YOUR_ORG_ID with your actual MongoDB Atlas organization ID. Ensure that your Pulumi MongoDB Atlas provider configuration is set up with the necessary credentials.