Brain Frame


Project Summary

Brain Frame is a project under St. Xaviers College where they were looking to work with Machine Learning. Hence, they decided to deploy a 2 Windows servers along with 2 RDS with MySQL, and SageMaker with S3 integration for Machine Learning where 1 windows server and RDS will be for long term use and the other windows server and RDS will be needed for 3 months. They additionally required 4 standard concurrent users for the 3 months environment.

Team Members

  • Kaushal Yadav
  • Shrey Singh
  • Shraddha Dhage
  • Suryansh Singh
  • Rahul Hanumante 

Estimated Time

Project started on 19th September 2022

SA Diagram


According to the client’s requirement, we have created a VPCs as Brain-Frame-VPC. In the VPC, we have created 2 public subnets for application and 4 private subnets for RDS databases.

We have launched 2 Application servers and 2 RDS simultaneously where the Application servers are deployed in public subnet and RDS is deployed privately within the private subnets. For security, in the security group rule, we have only whitelisted the client’s static IP for the RDP port and the endpoint of RDS server.

After the successful deployment of servers, we have configured SageMaker studio for Machine Learning in the Mumbai region. We have configured CloudWatch alerts to monitor the instance and RDS and send an email with SNS when an alert is triggered.
Budget alert is set according to total monthly consumption with the thresholds of 80% and 90% to monitor the Billing.

AWS Services used

Virtual private cloud (VPC)

Elastic compute cloud (EC2)

Simple Storage Service (S3)

Elastic block storage (EBS)

Relational Database Services (RDS)

SageMaker for Machine Learning

Identity and access management (IAM)

Simple notification service (SNS)




Scalability And Rapid Elasticity

Resiliency And Availability

On-Demand Self-Service

Easy Maintenance



Sage Maker offers accessibility to Machine Learning

It offers the preparation of data at scale

It accelerates Machine Learning development

It streamlines the Machine Learning lifecycle

It delivers High Performance

It offers Low-cost Machine Learning