Human Contact and Mistakes were reduced with Sagemaker & MLOps  

AWS FinTech Cloud Case Study Of PayMe

About the Client:

PayMe is the best personal loan app for online loans. The loan amount is disbursed to the bank with the least amount of paperwork possible. The loan approval system uses machine learning models, which predict whether a user is qualified to receive a loan based on the user’s criteria.

Business Need:

PayMe wanted an AWS machine learning solution to help their fintech company flourish. They aim to make use of existing AI power. The main objective is to improve the model’s accuracy and use the AWS cloud with its low cost, high performance, monitoring, and scalability. This company has already implemented a machine learning model, but as time passes, data grows, and older machine learning models are not accurate and scalable.


  • AWS Sagemaker was suggested since it is an extremely dependable managed solution for keeping machine learning workloads in AWS. 
  • In accordance with the best practices, a distinct network was built using a combination of VPC and Subnets. 
  • Sagemaker was introduced in a secure subnet. 
  • Data and model artifacts would be kept on Amazon S3.
  • Exploratory data analysis, data visualization, data processing model training, evaluation, and Sagemaker pipeline creation would all be done using Jupyter Notebooks based on Amazon Sagemaker. 
  • Investigating various algorithms using Sagemaker model training and evaluation to discover the best algorithm for the problem. 
  • Creating the Sagemaker pipeline based on the code for the model training, evaluation, and deployment that has been finalized. 

Reaping Rewards:  

  • AWS Sagemaker offered centralized solutions for all business needs related to machine learning, including interactive notebooks, powerful instances, model monitoring, and automatic re-training. 
  • Sagemaker MLOps decreased the human interaction labor needed for model re-training and monitoring since it was now performed automatically. 
  • By using these robust machine learning-based solutions, the model can now approve or reject loans based on the information provided by the consumer, reducing human contact and mistake.