Reduction in human intervention
Reduction in provisioning time
Improvement in customer satisfaction
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.
Amazon SageMaker
AWS CloudFormation
Amazon Simple Storage Service
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.
