Seamless Deployment of Machine Learning Applications on AWS with GitHub Actions: A Comprehensive Guide — PART 4

Part 4: Advanced Deployment Topics, Rollbacks, and Monitoring

1. Scaling Your Deployment for Production

Scaling is critical when transitioning from a development environment to a production system. Machine learning applications often experience variable loads and require dynamic resource allocation.

1.1 Auto-Scaling Strategies on AWS

Auto-scaling Groups (ASG):
When deploying on AWS EC2, configure Auto Scaling Groups to automatically add or remove instances based on load. For example, if your ML API experiences a spike in requests, additional EC2 instances can be provisioned to handle the increased traffic.

ECS/EKS Auto-scaling:
For containerized deployments on Amazon ECS or EKS, you can use Service Auto Scaling. This allows your container services to automatically adjust the number of running tasks or pods based on metrics like CPU utilization or request count.

Serverless Options:
AWS Lambda offers inherent scaling capabilities, where functions are invoked only as needed. This is especially useful for sporadic workloads or…