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End-to-end MLOps pipeline that catches LLM quality regressions before production. Every PR is scored against a versioned golden dataset using BERTScore + ROUGE-L + an LLM-as-Judge rubric, compared to the MLflow production baseline, and shadowed against 5% of live traffic. FastAPI + Celery + TimescaleDB + Streamlit + DVC + GitHub Actions.
Develop a chatbot that can effectively adapt to context and topic shifts in a conversation, leveraging the Stanford Question Answering Dataset to provide informed and relevant responses, and thereby increasing user satisfaction and engagement.
An end-to-end MLOps project for text summarization using the HuggingFace Pegasus model. Includes a full training pipeline, evaluation, and a FastAPI for deployment.