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Large-scale datasets and benchmarks for training, evaluating, and testing models to measure
Comprehensive Text Extraction and Document Processing Solutions for Users
An end-to-end MLOps pipeline for a production-grade fraud detection model. This project demonstrates best practices including data versioning (DVC), experiment tracking (MLflow), CI/CD (GitHub Actions), containerization (Docker), deployment on GKE, and advanced model analysis (poisoning attacks, drift, fairness, explainability).