Project Title: Cat & Dog Image Classifier

Implemented a Cat & Dog Image Classifier project comprising the following components:

Developed a robust Machine Learning (ML) model API using FastAPI and TensorFlow. The API accepts image inputs and provides predictions on the uploaded images, indicating whether the image contains a cat or a dog. Utilized Docker to containerize the ML model API, ensuring scalability and easy deployment. Frontend Angular Application:

Implemented functionalities allowing users to upload images and send them to the ML model API for classification. Containerized the frontend application using Docker, ensuring consistency across environments. NGINX Reverse Proxy Container:

To manage communication between the ML model API and the frontend application. Handled routing for user requests and ensured seamless integration between the frontend and backend components. Deployment and Networking:

Hosted Docker images on Docker Hub, facilitating easy access and distribution. Leveraged Docker networking to enable efficient communication between the ML model API and the frontend application, ensuring high performance and scalability. Key Technologies Used:

Successfully implemented an end-to-end Cat & Dog Image Classifier project, providing accurate predictions for uploaded images. Demonstrated proficiency in full-stack development, containerization, and deployment using industry-standard tools and technologies. Leveraged Docker and NGINX to optimize project scalability, performance, and maintainability.