Human Contact and Mistakes were reduced with Sagemaker & MLOps  

AWS FinTech Cloud Case Study Of PayMe
AWS FinTech Cloud Case Study Of PayMe

Human Contact and Mistakes were reduced with Sagemaker & MLOps  

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 makes use of machine learning models, which predict whether a user is qualified to receive a loan or not based on the user’s specified criteria. 

Business Need:

Blox wanted an AWS machine learning solution that can 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 goes on, 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 best practises, 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 visualisation, 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 finalised. 

Reaping Rewards:  

  • AWS Sagemaker offered centralised solutions for all business needs related to machine learning, including interactive notebooks, powerful instances, model monitoring, and automatic re-training. 
  • Sagemaker MLOps decreased the amount of human interaction labour 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.