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mlops-pipeline

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Developed an image classification web app using CNN to differentiate cats and dogs. Achieved high accuracy, precision, recall, and F1 score. Pipeline involves data preprocessing, model training, Docker deployment on AWS ECS, user-friendly interface, and reliable CI/CD. Showcases deep learning's potential in image analysis.

  • Updated Jan 22, 2024
  • Jupyter Notebook

End-to-end MLOps pipeline for hotel booking demand forecasting. Includes modular components for data ingestion, model training, evaluation, versioning, and deployment. Features configuration-based execution, CI/CD with GitHub Actions, and automated logging and testing.

  • Updated Apr 20, 2025
  • Jupyter Notebook

A complete production-ready MLOps framework with built-in distributed training, monitoring, and CI/CD. Deploy ML models to production with confidence using our battle-tested infrastructure.

  • Updated May 30, 2025
  • Python

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