ACM Projects' Fall 2024 AWS Workshop Demo

Resources you'll need as you go through the workshop

• Download the needed Iris data set from UC Irvine's Machine Learning Repository here and unpack into Jupyter project in AWS SageMaker
• Download the machine learning model notebook example here
  • Upload into the SageMaker Jupyter
• Copy the Lambda Function example below and paste the code into the Lambda function in AWS

  import json
  
  import boto3
  import ast
  
  def lambda_handler(event, context):
    
    runtime_client = boto3.client('runtime.sagemaker')
    
    endpoint_name = 'xgboost-2024-10-20-20-12-30-397'
    
    sample = '{},{},{},{}'.format(ast.literal_eval(event['body'])['x1'],
                                ast.literal_eval(event['body'])['x2'],
                                ast.literal_eval(event['body'])['x3'],
                                ast.literal_eval(event['body'])['x4'])
    
    response = runtime_client.invoke_endpoint(EndpointName = endpoint_name,
                                    ContentType = 'text/csv',
                                    Body = sample)
    
    result = int(float(response['Body'].read().decode('ascii')))
    
    print(result)
    
    return {
        'statusCode': 200,
        'headers': {
            'Access-Control-Allow-Origin': '*'
        },
        'body': json.dumps({'prediction' : result})
    }
  
  

Further information can be found in the tutorial video here that this workshop demo is based on