This repository contains the code for deploying a simple ML model using a pre-trained model and serving predictions through an API endpoint.
Objectives Deploy a simple ML model using a pre-trained model and serve predictions through an API endpoint. Implement an API server using a Python web framework like Flask or FastAPI. Set up a GitHub Actions workflow to automate model testing and deployment on each push to the repository. Build a Docker container for the API server with the necessary dependencies and the pre-trained model. Utilize Docker Hub to store the Docker image. Deploy the Docker container on AWS EC2. Set up an API Gateway to expose the API endpoint securely. Steps Clone the Repository: Clone this Git repository to your local machine.
bash Copy code git clone https://github.com/Azim-js/Bike_share_api.git Set Up Environment: Ensure you have Python installed on your machine. Create and activate a virtual environment using virtualenv or conda.
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virtualenv venv source venv/bin/activate Install Dependencies: Install the required Python packages by navigating to the project directory and running:
bash Copy code pip install -r requirements.txt Implement API Server: Implement the API server using a Python web framework like Flask or FastAPI. You can find the server code in the repository.
GitHub Actions Workflow: Set up a GitHub Actions workflow to automate model testing and deployment on each push to the repository. Refer to GitHub Actions documentation for setting up workflows.
Build Docker Container: Build a Docker container for the API server with the necessary dependencies and the pre-trained model. Use the Dockerfile provided in the repository.
bash Copy code docker build -t bike_share_api . Docker Hub: Push the Docker image to Docker Hub for easy access and deployment.
bash Copy code docker tag bike_share_api your_dockerhub_username/bike_share_api docker push your_dockerhub_username/bike_share_api Deploy on AWS EC2: Deploy the Docker container on AWS EC2. You can SSH into your EC2 instance and run the Docker container.
Set Up API Gateway: Set up an API Gateway on AWS to expose the API endpoint securely. Configure the API Gateway to route requests to the EC2 instance where the Docker container is running.
Usage After deployment, you can access the API endpoint securely through the configured API Gateway URL. Make POST requests to the API endpoint with input data to get predictions from the ML model.