Add final project documentation

Author: gbadedata

README.md

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+# Production-Style ML Inference API with FastAPI, Docker, Prometheus, Grafana, GitHub Actions, Kubernetes, HPA, and Ingress
+
+## Project Summary
+
+This project demonstrates how to build a production-style machine learning inference service from the ground up. It starts with offline model training and artifact serialization, then exposes the model through a FastAPI application with health checks, Prometheus metrics, automated tests, containerization, CI/CD, Kubernetes deployment, horizontal pod autoscaling, and ingress-based routing.
+
+The goal of this project is not just to train a model, but to show the engineering practices required to operate a machine learning model as a service.
+
+## What This Project Includes
+
+- offline model training and artifact saving
+- FastAPI inference API
+- request and response validation with Pydantic
+- liveness and readiness endpoints
+- Prometheus metrics exposure
+- automated testing with pytest
+- Docker containerization
+- Docker Compose observability stack with Prometheus and Grafana
+- load testing with Locust
+- GitHub Actions CI/CD
+- Docker image publishing to GitHub Container Registry (GHCR)
+- Kubernetes deployment and service routing
+- resource requests and limits
+- Horizontal Pod Autoscaler (HPA)
+- ingress-based access using ingress-nginx
+
+## Tech Stack
+
+- Python
+- FastAPI
+- scikit-learn
+- NumPy
+- joblib
+- pytest
+- Docker
+- Docker Compose
+- Prometheus
+- Grafana
+- Locust
+- GitHub Actions
+- GitHub Container Registry (GHCR)
+- Kubernetes
+- ingress-nginx
+
+## Architecture
+
+### Local and container architecture
+
+Client  
+→ FastAPI API  
+→ Model artifact  
+→ `/metrics`  
+→ Prometheus  
+→ Grafana  
+
+### Kubernetes architecture
+
+Client  
+→ Ingress  
+→ Kubernetes Service  
+→ FastAPI Pods  
+→ Model artifact  
+
+### Delivery architecture
+
+GitHub Push  
+→ GitHub Actions  
+→ Run Tests  
+→ Build Docker Image  
+→ Push to GHCR  
+→ Kubernetes pulls image  
+
+## API Endpoints
+
+### `GET /health/live`
+Returns application liveness status.
+
+Example response:
+
+```json
+{"status":"alive"}
+
+
+GET /health/ready
+
+Returns readiness status after model loading.
+
+Example response:
+
+{"status":"ready"}
+POST /predict
+
+Runs inference using the trained model.
+
+Example request:
+
+{
+  "features": [5.1, 3.5, 1.4, 0.2]
+}
+
+Example response:
+
+{
+  "prediction": 0
+}
+
+GET /health/live  
+GET /health/ready  
+POST /predict  
+GET /metrics  
+GET /docs
+
+## Project Structure
+
+ml_inference_api/
+├── .github/
+├── app/
+├── model/
+├── tests/
+├── monitoring/
+├── k8s/
+├── load_tests/
+├── scripts/
+├── docs/
+│ ├── evidence/
+│ ├── reports/
+│ └── architecture/
+├── Dockerfile
+├── docker-compose.yml
+├── requirements.txt
+├── requirements-dev.txt
+├── pytest.ini
+└── README.md
+
+## Evidence
+
+Project verification screenshots are stored in:
+
+docs/evidence/
+
+Evidence mapping:
+
+docs/evidence/evidence_index.md