During October 2025, Burak Poyraz developed a complete end-to-end medical Named Entity Recognition workflow in the JohnSnowLabs/spark-nlp-workshop repository. He trained a BERT-based model for medical NER using PyTorch and Transformers, then exported it to ONNX for cross-platform deployment. The workflow integrated Spark NLP Healthcare, enabling reproducible pipelines that streamline installation, data loading, training, evaluation, and ONNX export. Burak validated the solution with comprehensive end-to-end pipeline tests and updated documentation to support onboarding and reproducibility. His work established a production-ready, portable blueprint for healthcare NLP tasks, demonstrating depth in data engineering, MLOps, and practical application of deep learning.
During 2025-10, delivered a complete end-to-end Medical NER workflow with ONNX export and Spark NLP Healthcare integration in the JohnSnowLabs/spark-nlp-workshop repository. The work enables a reproducible, production-ready pipeline for healthcare NLP that can be deployed across platforms via ONNX, reducing deployment friction and accelerating iteration cycles. It also establishes a reusable blueprint for similar domain-specific NER tasks.
During 2025-10, delivered a complete end-to-end Medical NER workflow with ONNX export and Spark NLP Healthcare integration in the JohnSnowLabs/spark-nlp-workshop repository. The work enables a reproducible, production-ready pipeline for healthcare NLP that can be deployed across platforms via ONNX, reducing deployment friction and accelerating iteration cycles. It also establishes a reusable blueprint for similar domain-specific NER tasks.
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