Deploy Machine Learning Models on AWS Elastic Beanstalk
Running servers locally is good when getting started but during production, it has to be used or access everywhere which means the server has to be deployed in a cloud platform. We will be using Elastic Beanstalk for server deployment in this blog.
AWS Elastic Beanstalk is an easy-to-use service for deploying and scaling web applications and services developed with Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker on familiar servers such as Apache, Nginx, Passenger, and IIS.
You can simply upload your code and Elastic Beanstalk automatically handles the deployment, from capacity provisioning, load balancing, auto-scaling to application health monitoring. At the same time, you retain full control over the AWS resources powering your application and can access the underlying resources at any time
This is the initial folder content which only consists of a python script “application.py” followed by a pkl machine learning model and a requirements.txt file (which consists of the python libraries used within the “application.py” script). This folder contains code for lung cancer prediction.
