Developed an end-to-end medical Named Entity Recognition workflow for the JohnSnowLabs/spark-nlp-workshop repository, focusing on reproducibility and production readiness in healthcare NLP. The solution involved training a BERT-based model using PyTorch and exporting it to ONNX format, enabling seamless integration with Spark NLP Healthcare for cross-platform deployment. The workflow included comprehensive steps for installation, data loading, model training, evaluation, and ONNX export, along with robust pipeline testing to ensure reliability. Enhanced documentation and onboarding materials were provided to facilitate team adoption, establishing a reusable blueprint for domain-specific NER tasks using Python, Jupyter Notebook, and Spark NLP.
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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