FinTech Startup
AI-native ML pipeline from scratch
MLOps consulting and AI-native infrastructure for real-time fraud detection in fintech
The Challenge
Manual model deployment and no AI monitoring in fintech infrastructure
The data science team had built a fraud detection model in a Jupyter notebook that was manually re-trained once a month and deployed by copying files to an EC2 instance over SSH. There was no versioning, no model monitoring, and no way to know when the model started degrading. A regulatory audit flagged the lack of reproducibility as a compliance risk in their fintech infrastructure.
The Solution
AI-native MLOps platform with automated training and drift detection on GCP
We delivered an MLOps consulting engagement and built an AI-native infrastructure platform on GCP using Vertex AI Pipelines, MLflow for experiment tracking, and Feast as the feature store. The model training pipeline was fully automated: triggered by data drift alerts from Evidently AI, the pipeline retrains, validates against a holdout set, and promotes to production only if performance thresholds are met. All model versions, parameters, and datasets are tracked and auditable.
The Outcome
80% fraud detection accuracy and 50% lower AI infrastructure costs
Fraud detection accuracy improved by 80% within two model iterations once continuous training was in place. Model updates that used to take 3 weeks of manual work now complete in under 4 hours. Infrastructure costs dropped 50% by moving from always-on GPU instances to on-demand Vertex AI pipeline runs. The compliance team signed off on the audit trail within one sprint.
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