27%
Cost optimization through automation
14 models
ML models streamlined for real-time personalization
A Leading Financial Institution: MLOps at Scale with Kubeflow, GKE, and BigQuery
About Client
A leading financial institution managing large-scale personalization efforts across multiple products and platforms using 14 machine learning models.
Business Challenge
The financial institution faced challenges with high infrastructure costs, long turnaround times for model retraining, and error-prone manual processes for managing a complex ML ecosystem.
Goal
To optimize the personalization framework, reduce retraining and deployment times, lower costs, and improve the scalability and accuracy of ML models by implementing an MLOps approach.
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