CASE STUDY May 27, 2025
A leading US-based financial services provider wanted to scale its AI capabilities while reducing operational inefficiencies. With over 160 models across risk, fraud, and marketing, the client faced challenges with inconsistent deployment workflows, rising infrastructure costs, and a lack of AI observability. They partnered with Tiger Analytics to modernize their MLOps and DLOps framework for enterprise-wide scalability and compliance.
Tiger Analytics delivered a 30% reduction in operational costs, enabled faster deployment for 30+ AI use cases, and laid the foundation for scaling 160+ models. The transformation also included real-time AI observability, robust governance, and a future-ready architecture using Azure ML, Databricks MLflow, and Hugging Face.
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