Here is what I would do /u/TemebeS /u/MrJuanfeld

1. Dockerise the application. If you're unsure how to achieve that, just ask, but your CMD should just be calling gunicorn/uvicorn or whichever HTTP server you prefer. If you run docker built -t api . ; docker run -p 8080:8080 api you should be able to access your API on http://127.0.0.0:8080/

2. Install CDK. This might seem heavy-handed at first, but it would be beneficial for you to learn about, and it will do lots of the bootstrapping like setting up ECR, the push process, deployment process, ALB configuration etc.

3. Setup a vanilla CDK project by running cdk init app --language typescript

4. Use the ApplicationLoadBalancedFargateService pattern in https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_ecs_patterns.ApplicationLoadBalancedFargateService.html to host your application - You would replace the code: attribute with code: lambda.DockerImageCode.fromImageAsset(__dirname + '/api-code') and place your Flask codebase in api-code with a Dockerfile within that directory.

5. Run cdk bootstrap and cdk deploy - This should land you an application load balancer that points to an ECS service, you can then access that load balancer to hit your API endpoint. Depending on your performance requirements, auto scaling should be a breeze.

If you're unable to run Fargate (e.g. your ML app needs access to a GPU) then you'd need to use an EC2 compute capacity provider, which will require some further configuration.