Customer Challenge
A large banking company is running multiple risk, fraud, and credit line increase Machine Learning (ML) Models on virtual machines (VMs) in its on-premises data center. There was no ability to build and deploy code on-demand and no environment parity between model training and production. Capabilities of the current infrastructure imposed limitations on accuracy, leading to bias and pattern misidentification during recurring cycles. The customer needed an enterprise-grade end-to-end automated data science solution with the flexibility to run on-premises, in multiple public clouds, and support every stage of the ML lifecycle