Reference Architectures

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import * as cdk from 'aws-cdk-lib';
import * as lambda from 'aws-cdk-lib/aws-lambda';
import * as apigateway from 'aws-cdk-lib/aws-apigateway';
import * as dynamodb from 'aws-cdk-lib/aws-dynamodb';

export class ServerlessMLStack extends cdk.Stack {
  constructor(scope: cdk.App, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    // Feature store with on-demand scaling
    const featureStore = new dynamodb.Table(this, 'FeatureStore', {
      partitionKey: { name: 'entityId', type: dynamodb.AttributeType.STRING },

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import boto3
import json
from typing import List, Dict

sagemaker_runtime = boto3.client('sagemaker-runtime')
feature_store = boto3.client('sagemaker-featurestore-runtime')
dynamodb = boto3.resource('dynamodb')
cache_table = dynamodb.Table('recommendation-cache')

def lambda_handler(event, context):
    user_id = event['pathParameters']['userId']
    num_recommendations = int(event.get('queryStringParameters', {}).get('limit', 10))

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import boto3
import json
from functools import lru_cache
from aws_lambda_powertools import Logger, Tracer, Metrics
from aws_lambda_powertools.utilities.typing import LambdaContext
from aws_lambda_powertools.metrics import MetricUnit

logger = Logger()
tracer = Tracer()
metrics = Metrics()

# Tenant-specific model endpoints

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# Step Functions State Machine Definition (CDK)
from aws_cdk import (
    aws_stepfunctions as sfn,
    aws_stepfunctions_tasks as tasks,
    aws_lambda as lambda_,
    Duration
)

class ComputerVisionPipeline(Construct):
    def __init__(self, scope, id, **kwargs):
        super().__init__(scope, id, **kwargs)