case study

Machine Learning Lifecycle Management

Banking & Financial Services

Business Impacts

Reduction in human intervention

5 - 2 days

Reduction in provisioning time

Improvement in customer satisfaction

Customer Key Facts

  • Location : North America
  • Industry : Financial Services

Problem Context

The customer, an international financial services company, provides a diverse range of wealth accumulation and protection products and services. Their existing process for data preparation, model selection, and testing was manually driven and riddled with inefficiencies; taking up to five days in resource provisioning.




  • Integrating with third party solutions to automate the process of Amazon SageMaker resource deployment
  • Automating the process from data preparation to model selection and testing on Amazon SageMaker

Technologies Used

Amazon SageMaker

Amazon SageMaker

AWS CloudFormation

AWS CloudFormation

Amazon Simple Storage Service

Amazon Simple Storage Service

Automating Machine Learning Lifecycle Management for Increased Efficiency and Cost Savings

The customer sought to automate the process of providing Amazon SageMaker resources to end users; better enabling its developers and data scientists to create, train, and deploy machine-learning models in the cloud.


Quantiphi developed predefined cloud formation templates (CFTs) that deployed the necessary Amazon SageMaker resources based on a data scientist’s requirements. The solution includes a model leaderboard for performance evaluation, and uses a combination of custom-built pieces as well as the Autopilot, Automatic Model Tuning, Endpoints, and Model Monitor features of Amazon SageMaker.


  • Automated model training
  • Reduced manual intervention
  • Cost reduction

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