Mastering Serverless Computing for Data Science/

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Hands-On: Custom ML Docker Images in AWS Lambda

Hands-On: Custom ML Docker Images in AWS Lambda

Implementing custom ML dockers in AWS Lambda.

We'll cover the following...

Deployment

We have deployed our service the same way as before both in development and production. What SLS will do differently this time is build our Docker image and push it to ECR next to other resources that it will deploy to AWS.

Test yourself

Replace the value of the SERVERLESS_SERVICE environmental variable with the name of your choice. 

import joblib
import numpy as np
import json

model_file = "/opt/ml/model.joblib"
model = joblib.load(model_file)

def handler(event, context):
    x = event["body"]
    prd = model.predict(x)
    body = {
        "service": "sls-aws-python-starter-ml-serving-sf",
        "model": "linear",
        "version": "1.0",
        "prd": prd.tolist(),
    }

    return {
        "statusCode": 200,
        "body": body,
    }
Test yourself here

As a result, ...