1. Logging for AI Applications using Datadog AWS Integration


    In order to set up logging for AI applications using the Datadog AWS integration with Pulumi, you'll need to follow a few steps:

    1. Integrate AWS with Datadog: Set up the AWS integration within your Datadog account. This involves configuring the necessary permissions on AWS for Datadog to access the logs you're interested in.

    2. Configure Log Collection: Define which specific AWS services' logs you want to collect and forward to Datadog. Datadog will ingest these logs for monitoring and analytics purposes.

    3. Setup Monitoring and Alerts: Within Datadog, you can set up various monitors and alerts based on the log data you are collecting. This can be helpful for tracking the performance of your AI applications and getting alerts on specific conditions.

    Let's write a Pulumi program in Python to automate the integration of AWS with Datadog for log collection. This program assumes you've already set up both AWS and Datadog and have the appropriate access keys and permissions to make the necessary API calls.

    First, you'll need to install the required Pulumi packages for AWS and Datadog:

    pulumi plugin install resource datadog v4.23.0 pulumi plugin install resource aws v6.13.3

    Here's the Pulumi Python program:

    import pulumi import pulumi_datadog as datadog import pulumi_aws as aws # Replace these variables with your Datadog and AWS credentials datadog_api_key = "DATADOG_API_KEY" datadog_app_key = "DATADOG_APP_KEY" aws_access_key = "AWS_ACCESS_KEY" aws_secret_key = "AWS_SECRET_KEY" aws_account_id = "AWS_ACCOUNT_ID" # Configure the AWS provider with your credentials aws_provider = aws.Provider("aws_provider", access_key=aws_access_key, secret_key=aws_secret_key, region="us-east-1" # specify your AWS region ) # Configure the Datadog provider with your credentials datadog_provider = datadog.Provider("datadog_provider", api_key=datadog_api_key, app_key=datadog_app_key ) # Set up the AWS integration within your Datadog account datadog_aws_integration = datadog.aws.Integration("datadog_aws_integration", account_id=aws_account_id, role_name="DatadogAWSIntegrationRole", # Ensure this role exists with the correct permission in your AWS account filter_tags=["env:prod"], host_tags=["environment:production"], account_specific_namespace_rules={ "auto_scaling": False, # Example: Disable certain namespace rules "opsworks": False, }, opts=pulumi.ResourceOptions(provider=datadog_provider) ) # Setup logging for specific AWS services datadog_aws_integration_log_collection = datadog.aws.IntegrationLogCollection("datadog_aws_integration_log_collection", services=["s3", "lambda"], # Specify the services you want to collect logs from account_id=aws_account_id, opts=pulumi.ResourceOptions(provider=datadog_provider) ) # Exporting the Datadog Site URL for direct access to your Datadog dashboard pulumi.export("datadog_site_url", datadog_aws_integration_log_collection.id.apply( lambda _: "https://app.datadoghq.com/"))

    In the program above, we initialize two providers, one for AWS and another for Datadog, using the appropriate API keys and credentials. Ensure these match the ones you have.

    We create two resources: Integration, which is responsible for the AWS and Datadog integration setup, and IntegrationLogCollection, which specifies which services' logs to collect.

    Remember to replace the placeholder credentials with your actual API and access keys. You should ideally use environment variables or a secrets manager to handle credentials securely instead of hardcoding them in your code.

    Replace "DatadogAWSIntegrationRole" with the actual IAM role that you have created in AWS with the necessary permissions for Datadog to access the AWS services.

    The services array in IntegrationLogCollection should list the AWS services you want to collect logs for. For example, if your AI application is using S3 for storage and Lambda for computation, the above settings will collect logs from these services.

    After running this Pulumi program, logging will be set up, and logs will start flowing from the specified AWS services to Datadog, where you can monitor, visualize, and set up alerts based on the log data.