What the Client Was Facing
A major retailer had a recommendation engine model in a Jupyter notebook — updated manually every 3 months and deployed by a senior data scientist over 3 days. There was no monitoring to detect when the model was underperforming.
What ZippyOPS Was Engaged To Do
ZippyOPS was brought in to design and implement a solution addressing the root causes of the client's challenges — delivering measurable outcomes within a fixed engagement timeline. Our team worked embedded with the client's engineers throughout the entire project.
How We Solved It
ZippyOPS built an end-to-end MLOps pipeline on Vertex AI — automated training triggered by data drift, model evaluation gates with performance thresholds, A/B testing infrastructure for new model versions and Evidently for production monitoring. The model was containerised with BentoML and deployed to Cloud Run for low-latency inference.
Technologies Used
Measurable Outcomes Delivered
Model deployment time reduced from 3 days to 45 minutes
Automated retraining triggered when data drift exceeds threshold
A/B testing infrastructure enabling continuous improvement validated in production
Model performance monitoring catching degradation within 24 hours, not 3 months
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