1. Introduction This report provides a comprehensive guide to deploying a Machine Learning (ML) model using Docker, Kubernetes, and Minikube. The model is packaged as a Flask API and deployed on a local Kubernetes cluster.
2. Prerequisites Before starting, ensure the following tools are installed:  Python 3.9+  Docker  Minikube  Kubectl  Curl (for API testing)
3. Project Structure C:\ujwal
   │-- app.py │-- model.py │-- model.pkl │-- requirements.txt │-- Dockerfile │-- deployment.yaml │-- service.yaml
4. Build & Run the Docker Image cd "your dockerfile path" docker build -t ml-flask-app . docker run -p 5000:5000 ml-flask-app
5. Deploying with Kubernetes & Minikube 5.1. Start Minikube minikube start 5.2. Load the Docker Image into Minikube minikube image load ml-flask-app 5.3. Deploy the Application kubectl apply -f C:\Gururaj\deployment.yaml #your yaml file path kubectl apply -f C:\Gururaj\service.yaml #your yaml file path 5.4. Verify Deployment kubectl get pods kubectl get services
6. Accessing the Application 6.1. Get Service URL minikube service ml-flask-service --url Example Output: http://127.0.0.1:51813 6.2. Test API Endpoints Home Route curl http://127.0.0.1:51813/