1. Low-Latency Feature Store for Machine Learning Models


    Creating a low-latency feature store to serve machine learning models involves multiple components that ensure fast data retrieval and efficient model serving. In the cloud environment, this typically requires a database or data warehouse for storing features, a compute resource to process the data and serve the models, and possibly a registry to manage different versions of the models.

    With Pulumi, we can define and deploy our entire infrastructure in code, using our programming language of choice. In this example, I will show you how to create a low-latency feature store using Pulumi with AWS resources. We will use Amazon DynamoDB as the feature store because of its single-digit millisecond performance at any scale, AWS Lambda for compute to serve the machine learning model, and Amazon SageMaker to manage and deploy machine learning models.

    Here's an overview of the steps we'll follow:

    1. Create a DynamoDB table: This will be our feature store, where we'll keep features required by our machine learning models.
    2. Deploy a Lambda function: This function will act as a middleware to retrieve features from our feature store and pre-process them for model prediction.
    3. SageMaker Model: Although creating a full SageMaker workflow is out of this code's scope, I'll briefly explain where SageMaker fits in this architecture.

    The program I'm going to show you next will be a Python Pulumi program that defines the DynamoDB table and the Lambda function. For simplicity, the Lambda function in this example won't actually serve a machine learning model but you can easily extend it to do so.

    Let's start with the program:

    import json import pulumi import pulumi_aws as aws # Create a DynamoDB table to act as our feature store feature_store_table = aws.dynamodb.Table("featureStoreTable", attributes=[ aws.dynamodb.TableAttributeArgs( name="FeatureID", # Unique identifier for features type="S", # The attribute type is a string ), ], hash_key="FeatureID", billing_mode="PROVISIONED", # You can choose 'PAY_PER_REQUEST' for on-demand capacity pricing read_capacity=10, # Provisioned read capacity units write_capacity=10, # Provisioned write capacity units ) # Define the IAM role for the Lambda function lambda_role = aws.iam.Role("lambdaRole", assume_role_policy=json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com", }, }], }) ) # Attach the AWSLambdaBasicExecutionRole policy to the Lambda role lambda_role_policy_attachment = aws.iam.RolePolicyAttachment("lambdaRolePolicyAttachment", role=lambda_role.name, policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole" ) # Lambda function that retrieves features and serves the machine learning model feature_retriever_lambda = aws.lambda_.Function("featureRetrieverLambda", code=pulumi.FileArchive("./lambda"), # Directory where your Lambda function code is stored role=lambda_role.arn, handler="index.handler", # The entrypoint into your Lambda function code runtime=aws.lambda_.Runtime.PYTHON_3_8, # Specifying the Python 3.8 runtime ) # Give the Lambda function read access to the DynamoDB feature store lambda_dynamodb_access = aws.iam.RolePolicy("lambdaDynamoDBAccess", role=lambda_role.name, policy=feature_store_table.arn.apply(lambda arn: json.dumps({ "Version": "2012-10-17", "Statement": [{ "Action": [ "dynamodb:GetItem", "dynamodb:Query" ], "Effect": "Allow", "Resource": arn, }], })) ) # Expose the DynamoDB table name and Lambda function Arn as stack outputs pulumi.export('feature_store_table_name', feature_store_table.name) pulumi.export('feature_retriever_lambda_arn', feature_retriever_lambda.arn)

    In the program above, we created a DynamoDB table featureStoreTable with a simple primary key FeatureID. We also provisioned the table with a read and write capacity, which you can adjust based on your needs or you could use on-demand capacity mode instead.

    Then, we created an IAM role lambdaRole with the basic execution policy attached, which allows our Lambda function to write logs to CloudWatch.

    We defined a Lambda function featureRetrieverLambda, which is where you'll include the logic to interact with the machine learning model. For the Lambda function code, you would have a directory named "lambda" with an index.py file that contains the actual code (handler function).

    Finally, we added a policy lambdaDynamoDBAccess to grant our Lambda function read access to the DynamoDB feature store.

    Next steps would include writing the actual Lambda function logic to retrieve features and use them to serve the machine learning model, and setting up Amazon SageMaker to manage the machine learning models.

    The Lambda function in the AWS environment often serves as a compute layer that interacts with the feature store and the machine learning model, preprocessing the data if necessary, before sending it to the model for inference.

    Amazon SageMaker would be used to train, deploy, and manage your machine learning models. You could create a SageMaker endpoint using aws.sagemaker.Endpoint, and then your Lambda function would send the data to this endpoint for inference.

    You can extend the above Pulumi program to include SageMaker resources once you have your model ready to deploy. This setup ensures you have a low-latency, scalable infrastructure to serve machine learning models with a cloud-native solution.