Scaling applications with SageMaker deployment and AWS Autoscaling

Autoscaling is a crucial aspect of deploying ML models in production environments, ensuring that applications can handle varying workloads efficiently. Amazon SageMaker, combined with AWS Auto Scaling, provides a robust solution for automatically adjusting resources based on demand. In this section, you will explore different scenarios where autoscaling is essential and how to achieve it, using SageMaker model deployment options and AWS Auto Scaling.

Scenario 1 – Fluctuating inference workloads

In a retail application, the number of users making product recommendation requests can vary throughout the day, with peak loads during specific hours.

Autoscaling solution

Implement autoscaling for SageMaker real-time endpoints to dynamically adjust the number of instances, based on the inference request rate.

Steps

1. Configure the SageMaker endpoint to use autoscaling.
2. Set up minimum and maximum...