Image Search Engine Automation
End-to-end build of a machine learning image similarity engine that automates trademark conflict detection through continuous retraining and Kubernetes deployment.
Problem & Context
The European Union Intellectual Property Office (EUIPO) needed a system to identify potentially conflicting trademark designs as part of its registration process. Before this project, no similarity searches relied on manually added tags to images, which was inefficient and often missed visually similar designs. The goal was to design and build from scratch a robust image similarity engine powered by a TensorFlow model, capable of retrieving similar designs for any new registration request. The system needed to continuously learn from newly registered designs, automatically retrain and redeploy updated models on a daily basis, and scale reliably in production.
Responsibilities
- Supported development from model training through deployment infrastructure.
- Partnered with data scientists on feature extraction and model selection tailored to trademark imagery.
- Implemented automated ingestion and preprocessing with Python and OpenCV for new designs.
- Built incremental TensorFlow training pipelines with Metaflow to avoid full retrains.
- Authored Apache Airflow DAGs orchestrating daily training, validation, packaging, and rollout.
- Established CI/CD with Jenkins and Argo for container builds and Kubernetes deployments.
- Defined monitoring, logging, and fault-handling patterns for high reliability.
Architecture & Stack
- Languages & Frameworks: Python, TensorFlow, OpenCV
- Model Pipeline: Feature extraction + similarity embeddings with incremental transfer learning
- Orchestration: Apache Airflow DAGs and Metaflow-managed ML flows
- CI/CD: Jenkins + Argo automating model packaging and rollout
- Containerization & Hosting: Dockerized inference services on Kubernetes with rolling updates
- Data Pipeline: Automated retrieval, preprocessing, and augmentation of new trademark imagery
- Monitoring & Reliability: Integrated logging, alerting, and error handling across Airflow and Kubernetes
Outcomes
- Delivered EUIPO’s first production image similarity engine for automated conflict detection.
- Implemented daily retraining and redeployment so the model stays current with new filings.
- Eliminated manual retraining and deployment steps, shrinking update cycles from days to hours.
- Raised search accuracy and examiner productivity by surfacing visually similar designs quickly.
- Established a scalable MLOps foundation now integral to EUIPO’s examination workflow.
Learn More
Official product page: euipo.europa.eu ↗