Practical deep learning using AWS SageMaker, AWS Lambda, and API Gateway.
In the previous article, we go through the basics of SageMaker on training, testing, and deployment of a machine learning model. We are particularly using the XgBoost sample notebook to predict potential customers that are most likely to convert based on customer and aggregate level metrics.
Serving Machine Learning Model using AWS SageMaker and Boto3
Learn how to serve machine learning model using AWS SageMaker and the Python Boto3 library.
In this article, let’s use a deep learning model provided by AWS SageMaker, and expose the endpoint leveraging Lamba and API Gateway.
Deploy a Deep Learning Model
I am going to deploy
YOLOv3 Object Detector from the AWS Marketplace
This model can detect multiple objects on the input image. The results include category names, confidence scores, and absolute locations on the input image.
Once deployed, you should be able to see the model endpoint.
Inference using Boto3
Using the below code snippet, I can invoke the endpoint to make inferences.