1. Real-time Feature Store for Machine Learning on AWS MemoryDB


    In order to create a real-time feature store for machine learning on AWS, using MemoryDB for Redis as a database to store real-time features, we'll use Pulumi, an Infrastructure as Code tool. This infrastructure typically includes the following AWS resources:

    1. AWS MemoryDB for Redis: a Redis-compatible, in-memory database that delivers ultra-fast performance for workloads that require microsecond responsiveness.
    2. IAM Roles and Policies: to define permissions for actions that MemoryDB and other services need to interact with each other.
    3. Asset or Archive Publishing: to publish machine learning models or feature transformers, usually as Lambda functions or container images.

    For brevity, I will focus on creating the AWS MemoryDB cluster for storing the machine learning features, but not on machinery for publishing and using machine learning models.

    Below is a Pulumi program, written in Python, that creates a MemoryDB cluster which can be used as a feature store. It outlines:

    • The creation of an AWS MemoryDB Cluster.
    • Setting up an IAM Role with policies that allow necessary actions, such as reading and writing to the MemoryDB cluster.
    • Exporting relevant information that you might need to connect to your MemoryDB cluster.
    import pulumi import pulumi_aws as aws # Create an AWS VPC for the MemoryDB cluster, which requires an isolated network. vpc = aws.ec2.Vpc("vpc", cidr_block="", enable_dns_hostnames=True) # Create subnet groups for the MemoryDB for Redis. subnet_group = aws.memorydb.SubnetGroup("subnet-group", description="My cluster subnet group", subnet_ids=[subnet.id for subnet in vpc.public_subnets]) # Create an AWS Security Group for the MemoryDB cluster. security_group = aws.ec2.SecurityGroup("security-group", description="Allow access to MemoryDB", vpc_id=vpc.id, ingress=[ { "protocol": "tcp", "from_port": 6379, "to_port": 6379, "cidr_blocks": [""], }, ], egress=[ { "protocol": "-1", "from_port": 0, "to_port": 0, "cidr_blocks": [""], }, ]) # IAM Role and Policy for managing MemoryDB. iam_policy_document = aws.iam.get_policy_document(statements=[ { "actions": [ "memorydb:*", ], "resources": ["*"], } ]) memorydb_iam_policy = aws.iam.Policy("memorydb-iam-policy", name="MemoryDBPolicy", policy=iam_policy_document.json) memorydb_role = aws.iam.Role("memorydb-role", assume_role_policy=iam_policy_document.json) aws.iam.RolePolicyAttachment("memorydb-role-policy-attachment", role=memorydb_role.name, policy_arn=memorydb_iam_policy.arn) # Create the MemoryDB Cluster. memorydb_cluster = aws.memorydb.Cluster("memorydb-cluster", acl_name="open-access", node_type="db.r6g.large", subnet_group_name=subnet_group.name, security_group_ids=[security_group.id], num_shards=1, num_replicas_per_shard=1) # Export the MemoryDB cluster endpoint. pulumi.export("memorydb_cluster_endpoint", memorydb_cluster.primary_endpoint_address)

    In this program:

    • We begin by creating a new Virtual Private Cloud (VPC) with DNS hostname support enabled and CIDR block The VPC will contain networking resources that the MemoryDB cluster resides in.
    • Then, we set up the subnet and security groups necessary for the MemoryDB setup. The subnet needs to be associated with the VPC we created, and the security group is set to allow inbound connections on port 6379, the standard Redis port.
    • Next, we define an IAM policy that allows all actions (memorydb:*) on MemoryDB resources and attach it to a new IAM role.
    • After that, we create the MemoryDB cluster with the required configuration, such as specifying the node type and connecting it to the previously created subnet group and security group.
    • Finally, we export the MemoryDB cluster's primary endpoint address, which can be used to connect to the feature store from your machine learning applications or services.

    Remember to replace the CIDR blocks and security group settings with those that match your organization's security requirements.