1. AI Model Performance Tracking with MySQL Databases


    To set up AI model performance tracking, you would typically need a database where you can store and query your performance metrics. MySQL is a widely used relational database management system that would suit this purpose well. In the following Pulumi program, we will create a MySQL database instance using AWS's Relational Database Service (RDS). AWS RDS is a managed service that makes it easy to set up, operate, and scale a relational database in the cloud.

    Here are the steps our Pulumi program will follow:

    1. Create a new virtual private cloud (VPC) to keep the database secure within a private network.
    2. Create a subnet group for the RDS instance, as RDS instances need to be attached to subnets within your VPC.
    3. Provision an RDS instance that will run MySQL.
    4. Set up security groups to control access to the RDS instance.

    This will give you a solid foundation to start tracking the performance of AI models.

    The following Pulumi program in Python illustrates how this can be done:

    import pulumi import pulumi_aws as aws # Create a VPC for our RDS instance to ensure our data is secure within our own network. vpc = aws.ec2.Vpc("ai_performance_vpc", cidr_block="", instance_tenancy="default", enable_dns_hostnames=True, enable_dns_support=True, tags={ "Name": "ai_model_performance_vpc", }) # Create subnet groups for the RDS instance. # It's good practice to have the RDS instance in multiple availability zones for high availability. subnet_1 = aws.ec2.Subnet("ai_performance_subnet_1", vpc_id=vpc.id, cidr_block="", availability_zone="us-west-2a", tags={ "Name": "ai_model_performance_subnet_1", }) subnet_2 = aws.ec2.Subnet("ai_performance_subnet_2", vpc_id=vpc.id, cidr_block="", availability_zone="us-west-2b", tags={ "Name": "ai_model_performance_subnet_2", }) subnet_group = aws.rds.SubnetGroup("ai_performance_subnet_group", subnet_ids=[subnet_1.id, subnet_2.id], tags={ "Name": "ai_model_performance_subnet_group", }) # Create a security group to control who can access the RDS instance. security_group = aws.ec2.SecurityGroup("ai_performance_sg", vpc_id=vpc.id, description="Allow inbound traffic", ingress=[ { "protocol": "tcp", "from_port": 3306, # Default MySQL port "to_port": 3306, "cidr_blocks": [""], }, ], egress=[ { "protocol": "-1", "from_port": 0, "to_port": 0, "cidr_blocks": [""], }, ], tags={ "Name": "ai_model_performance_sg", }) # Create the MySQL RDS instance. mysql_rds_instance = aws.rds.Instance("ai_performance_mysql_instance", allocated_storage=20, storage_type="gp2", engine="mysql", engine_version="8.0.20", instance_class="db.t2.micro", name="ai_performance_db", username="admin", password="password", # Replace with a secure password or use Pulumi's secrets handling. parameter_group_name="default.mysql8.0", db_subnet_group_name=subnet_group.id, vpc_security_group_ids=[security_group.id], skip_final_snapshot=True, tags={ "Name": "ai_performance_mysql_instance", }) # Export the endpoint of the RDS instance so that it can be used to access the database. pulumi.export('mysql_rds_endpoint', mysql_rds_instance.endpoint)

    Keep in mind these key points in the program:

    • Replace "password" with a secure password or use Pulumi's secret management to avoid having a plain text password in your code.
    • The MySQL RDS instance created will be on the free tier (db.t2.micro) with the default configurations.

    This program will provision the necessary AWS resources to host your MySQL database. Once the RDS instance is available, you can connect to it with any standard MySQL client using the database endpoint, username, and password. You can then create the tables you need to track the performance of your AI models. Remember that the sample program skips the final database snapshot creation with skip_final_snapshot, so you may want to change this behavior for a production setup.