What the Client Was Facing
A fintech company's fraud detection model was updated by a manual process run every 2–3 weeks by a single senior engineer. The process was fragile, had caused two production outages and the model had no real-time performance monitoring.
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 implemented a CI/CD pipeline for ML using ZenML and GitHub Actions. Model training, evaluation and deployment were fully automated. Seldon Core on Kubernetes handled production serving with A/B traffic splitting between model versions. Grafana dashboards monitored precision, recall and false positive rate in real time.
Technologies Used
Measurable Outcomes Delivered
Model deployment time reduced from 2–3 weeks of manual work to 2 hours automated
Zero deployment outages since implementation — safe canary deployment strategy
Real-time model performance monitoring — degradation detected within hours, not weeks
False positive rate reduced 23% through faster iteration enabled by automated pipeline
Want Similar Results for Your Team?
Book a free consultation and let's discuss how ZippyOPS can deliver the same transformation for your organisation.