1. Integrating MongoDB Atlas Data Lake with AI Model Training Workflows


    Integrating MongoDB Atlas Data Lake with AI model training workflows involves several steps. First, you need to set up a data lake in MongoDB Atlas which will store your datasets. Afterward, you would typically use this data for training machine learning models, which might involve different cloud providers and services for computation.

    Pulumi can help you orchestrate and manage such workflows by using infrastructure as code. Below, you'll find a program that demonstrates how to create a MongoDB Atlas Data Lake resource. The data lake can be connected with an AI model training workflow, which would depend on the specific AI services and cloud providers you choose to use for model training.

    In this example, we are going to set up the MongoDB Atlas Data Lake. You need to have a MongoDB Atlas account and a project created. The mongodbatlas.DataLake resource will specify the configuration for the data lake within the project. For the AI model training part, we are not detailing its setup since it can vary significantly depending on the tools and cloud services you plan to use (like AWS Sagemaker, GCP AI Platform, Azure Machine Learning, etc.). However, once the data lake is in place, you would use its URI and possibly credentials as part of your AI model training service configuration.

    Here's how you could define the data lake using Pulumi with Python:

    import pulumi import pulumi_mongodbatlas as mongodbatlas # MongoDB Atlas Data Lake configuration requires an existing project. # Replace `project_id` with your actual MongoDB Atlas Project ID. project_id = "your-mongo-project-id" # Configure AWS credentials to give data lake access to your S3 buckets. # These should be set according to your actual AWS setup. # Securely handling these values is important, and you would typically use # Pulumi's configuration system or another secret management approach. aws_creds = mongodbatlas.DataLakeAwsArgs( role_id="your-aws-role-id", test_s3_bucket="your-test-s3-bucket-name" ) # Define a Data Lake resource in your project. data_lake = mongodbatlas.DataLake( "my-data-lake", project_id=project_id, aws=aws_creds, data_process_region=mongodbatlas.DataLakeDataProcessRegionArgs( cloud_provider='AWS', region='us-east-1' ), data_stores=[ mongodbatlas.DataLakeDataStoresArgs( name="my-data-store", data_store_type="S3", provider="AWS", region="us-east-1", bucket_name="my-s3-bucket", prefix="", delimiter="", include_tags=False ) ], storage_databases=[ mongodbatlas.DataLakeStorageDatabasesArgs( name="my-storage-database", views=[ mongodbatlas.DataLakeStorageDatabasesViewsArgs( name="my-view", source="my-source", # Additional view settings like pipeline or materialized go here. ) ] ) ], ) # Expose the Atlas Data Lake URI to use in other parts of the infrastructure, # such as AI model training services. pulumi.export('data_lake_uri', data_lake.endpoint)

    This Pulumi program sets up a MongoDB Atlas Data Lake with the provided configuration, including AWS credential settings to allow access to a specified S3 bucket and establishing a data processing region. The DataLakeDataStoresArgs and DataLakeStorageDatabasesArgs are used to define data stores and databases inside your data lake, respectively.

    Make sure to replace placeholder values (like 'your-mongo-project-id' or 'your-aws-role-id') with actual values from your MongoDB Atlas and AWS configurations. Also, remember to handle secrets like AWS credentials securely.

    Once your MongoDB Atlas Data Lake is set up, you would then configure your AI model training workflow to pull data from this lake. The specifics of this integration will vary depending on the machine learning platform and frameworks you are using. Generally, they would include setting up a compute resource, installing the necessary AI and machine learning libraries, and writing the code for model training which can access the data from your MongoDB Atlas Data Lake using the endpoint URL provided (data_lake_uri).