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.
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.