From Solo Deployment to Enterprise-Scale Reliability
A rapidly scaling industrial operation needed to implement high-accuracy automated visual inspection. The challenge was not just building the initial model, but creating a system that could sustain high accuracy (99.9%) across vast data volume, manage a constant flow of false positives, and function without dedicated MLOps staff. The system was prone to operational fatigue and manual bottlenecking.
I architected and executed the entire CV operational pipeline. My initial role evolved into founding and directing a 10-person specialized team focused on MLOps, continuous data validation, and model governance. This allowed us to shift from reactive fixes to proactive reliability management, treating the CV model as a core business asset, not a fragile experiment.
Automated Drift Detection: Designed and deployed proprietary dashboard trackers that continuously monitored input data characteristics and model performance, triggering automated retraining alerts based on pre-defined performance decay thresholds.
Human-in-the-Loop Optimization: Developed custom data post-processing tools that intelligently pre-filtered results, reducing the volume of manual human review by over 90%.
Resource Management & Growth: Scaled the team, infrastructure, and operational procedures to sustain production volume and accuracy, demonstrating the ability to manage resource allocation and customer/stakeholder communication.