What the Client Was Facing
A medical imaging company had a radiology AI model deployed as a static Docker container updated twice a year. When a new dataset became available or performance degraded, the update process was entirely manual, undocumented and required the original data scientist who had since left.
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 fully automated ML pipeline using Kubeflow Pipelines on GKE. DVC managed dataset versioning, MLflow tracked experiments and Evidently monitored production model performance. When drift was detected, automated retraining was triggered with quality gates evaluating performance before promotion.
Technologies Used
Measurable Outcomes Delivered
Model retraining automated — triggered when data drift detected, no manual intervention
Model deployment time reduced from 2 weeks to 3 hours
Full reproducibility — every model version linked to the exact dataset and code that produced it
Regulatory documentation generated automatically for each model version
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