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aws-deployment

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Deploy a scalable and secure Django blog on AWS! This project harnesses the power of AWS services like EC2, RDS, S3, DynamoDB, CloudFront, and Route 53 to create a robust web application. Users can seamlessly upload pictures and videos on their blog pages, with media content stored in an S3 bucket and metadata recorded on a DynamoDB table.

  • Updated Dec 6, 2023
  • Python
ML-Model-Deployment-in-AWS

This project deploys a diabetes prediction model on AWS using MLOps principles. It features a Flask-based UI for user interaction and utilizes CI/CD pipelines for automated deployment. By leveraging AWS infrastructure, the project ensures scalability, version control, and monitoring of the deployed model.

  • Updated Mar 31, 2024
  • HTML

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

๐— ๐—Ÿ ๐—ฝ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜, encompassing key topics like ๐——๐—ฎ๐—ด๐˜€๐—ต๐˜‚b and ๐— ๐—Ÿ๐—ณ๐—น๐—ผ๐˜„ for version control, ๐— ๐—Ÿ๐—ข๐—ฝ๐˜€ practices for efficient deployment, and robust ๐—–๐—œ/๐—–๐—— ๐—ฝ๐—ถ๐—ฝ๐—ฒ๐—น๐—ถ๐—ป๐—ฒ setup. Showcased ๐—”๐—ช๐—ฆ ๐—ฑ๐—ฒ๐—ฝ๐—น๐—ผ๐˜†๐—บ๐—ฒ๐—ป๐˜ with the help of ๐—š๐—ถ๐˜๐—›๐˜‚๐—ฏ ๐—”๐—ฐ๐˜๐—ถ๐—ผ๐—ป prowess for seamless machine learning application integration.

  • Updated Aug 2, 2023
  • Jupyter Notebook

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