
Developed and documented a feature for the mlflow/mlflow-website repository that guides users through building and managing an LLM-based OCR system using MLflow GenAI. The work focused on creating a reproducible workflow, including prompt engineering, debugging, and model evaluation, with practical Python code examples for data loading, MLflow tracking, and custom metric evaluation. Leveraging skills in Data Science, GenAI, and technical writing, the contribution culminated in a comprehensive blog post that serves as a reference implementation. This effort improved onboarding for developers and facilitated external adoption by making advanced OCR use cases more accessible and easier to experiment with.
September 2025 (2025-09) - mlflow/mlflow-website: Delivered and documented a key feature focused on LLM-based OCR workflows using MLflow GenAI. The initiative culminated in a comprehensive blog post that explains building, managing, and evaluating an OCR system powered by GenAI, including prompt iteration, debugging workflows, and model evaluation. The work includes practical code examples for data loading, MLflow tracking, prompting, and custom metric evaluation, providing a runnable reference for users to reproduce and adapt the workflow. Impact: Strengthened MLflow's GenAI storytelling and onboarding for developers, improved accessibility to advanced OCR use cases, and established a reference implementation that accelerates adoption and experimentation in production-like environments. Notes: All changes are scoped to the mlflow-website repository and documented with a clear commit trace for traceability.
September 2025 (2025-09) - mlflow/mlflow-website: Delivered and documented a key feature focused on LLM-based OCR workflows using MLflow GenAI. The initiative culminated in a comprehensive blog post that explains building, managing, and evaluating an OCR system powered by GenAI, including prompt iteration, debugging workflows, and model evaluation. The work includes practical code examples for data loading, MLflow tracking, prompting, and custom metric evaluation, providing a runnable reference for users to reproduce and adapt the workflow. Impact: Strengthened MLflow's GenAI storytelling and onboarding for developers, improved accessibility to advanced OCR use cases, and established a reference implementation that accelerates adoption and experimentation in production-like environments. Notes: All changes are scoped to the mlflow-website repository and documented with a clear commit trace for traceability.

Overview of all repositories you've contributed to across your timeline