README.md

---
title: AutoML MLOps Pipeline
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: docker
app_port: 8000
pinned: false
license: mit
---

# 🤖 AutoML MLOps Pipeline

Production-ready end-to-end AutoML pipeline with MLflow tracking, comprehensive monitoring, and automated orchestration.

[![CI Pipeline](https://github.com/Abeshith/AutoML-MLOps-PipeLine/actions/workflows/ci.yaml/badge.svg)](https://github.com/Abeshith/AutoML-MLOps-PipeLine/actions/workflows/ci.yaml)
[![Docker Build](https://github.com/Abeshith/AutoML-MLOps-PipeLine/actions/workflows/docker-build.yaml/badge.svg)](https://github.com/Abeshith/AutoML-MLOps-PipeLine/actions/workflows/docker-build.yaml)

## 🚀 Features

- **🤖 AutoML**: AutoGluon, FLAML, PyCaret integration
- **📊 MLflow Tracking**: DagsHub integration with comprehensive metrics
- **🔍 Monitoring**: Drift detection, prediction logging, performance tracking
- **📈 Observability**: Prometheus metrics & Grafana dashboards
- **🔄 Orchestration**: Airflow DAGs for automated scheduling
- **🐳 Docker**: Complete containerization with docker-compose
- **⚡ FastAPI**: RESTful API with 11+ endpoints
- **🎯 CI/CD**: GitHub Actions for automated testing and deployment

## 📋 Pipeline Stages

1. **Data Ingestion** - Load and validate dataset
2. **Data Validation** - Schema validation and quality checks
3. **Data Transformation** - Feature engineering and preprocessing
4. **AutoML Training** - Multi-framework model training
5. **Model Evaluation** - Comprehensive metrics and validation
6. **Model Comparison** - Best model selection
7. **Model Pusher** - Production model deployment

## 🛠️ Tech Stack

- **ML Frameworks**: AutoGluon, FLAML, PyCaret
- **API**: FastAPI, Uvicorn
- **Tracking**: MLflow, DagsHub
- **Monitoring**: Prometheus, Grafana, Evidently AI
- **Orchestration**: Apache Airflow
- **Containerization**: Docker, Docker Compose
- **CI/CD**: GitHub Actions

## 📦 Quick Start

### Local Development

```bash
# Clone repository
git clone https://github.com/Abeshith/AutoML-MLOps-PipeLine.git
cd AutoML-MLOps-PipeLine

# Create virtual environment
python -m venv automlenv
source automlenv/bin/activate  # On Windows: automlenv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set environment variables
cp .env.example .env
# Edit .env with your credentials

# Run training pipeline
python scripts/train.py

# Start API server
python scripts/serve.py --reload
```

### Docker Deployment

```bash
# Start all services
docker-compose up -d

# Access services
# API: http://localhost:8000/docs
# Prometheus: http://localhost:9090
# Grafana: http://localhost:3000 (admin/admin)
```

## 🌐 API Endpoints

### Prediction
```bash
POST /predict
{
  "age": 45,
  "sex": 1,
  "cp": 2,
  "trestbps": 130,
  "chol": 250,
  "fbs": 0,
  "restecg": 1,
  "thalach": 150,
  "exang": 0,
  "oldpeak": 2.5,
  "slope": 2,
  "ca": 0,
  "thal": 2
}
```

### Training
```bash
POST /train
GET /train/status
```

### Monitoring
```bash
GET /monitoring/metrics          # Prometheus metrics
GET /monitoring/health/drift     # Drift detection status
GET /monitoring/performance/summary
GET /monitoring/reports/daily
```

## 📊 Model Performance

- **Validation Accuracy**: 88.84%
- **Test Accuracy**: 88.68%
- **ROC-AUC**: 95.48%
- **Best Model**: WeightedEnsemble_L3

## 🔧 Utility Scripts

```bash
# Train model
python scripts/train.py

# Evaluate model
python scripts/evaluate.py --model-path <path>

# Start API server
python scripts/serve.py --host 0.0.0.0 --port 8000 --reload

# Initialize Airflow
python scripts/init_db.py
```

## 🔄 Airflow Orchestration

```bash
# Set AIRFLOW_HOME
export AIRFLOW_HOME=$(pwd)/airflow

# Initialize database
python scripts/init_db.py

# Start services
airflow scheduler  # Terminal 1
airflow webserver  # Terminal 2

# Access UI: http://localhost:8080
```

## 📈 Monitoring Stack

- **Drift Detection**: KS test for numerical features
- **Prediction Logging**: JSONL format with threading
- **Performance Tracking**: Batch-level metrics
- **Report Generation**: Daily/weekly JSON reports
- **Prometheus Metrics**: Request count, latency, accuracy, drift status
- **Grafana Dashboards**: 5-panel visualization

## 🐳 Docker Services

- **FastAPI App** (8000): Main ML API
- **Prometheus** (9090): Metrics collection
- **Grafana** (3000): Visualization dashboards

## 🔐 Environment Variables

```env
MLFLOW_TRACKING_URI=your_dagshub_uri
DAGSHUB_TOKEN=your_token
```

## 📚 Documentation

- [Docker Setup](DOCKER.md)
- [Scripts Usage](scripts/README.md)
- [CI/CD Workflows](.github/workflows/README.md)
- [Airflow Guide](airflow/README.md)

## 🧪 CI/CD Pipeline

### Automated Workflows
- **CI**: Lint with flake8, format check with black
- **Docker Build**: Build and push to GitHub Container Registry
- **HuggingFace Deploy**: Auto-deploy to Spaces on push

### Container Images
```bash
docker pull ghcr.io/abeshith/automl-mlops-pipeline:latest
```

## 📊 Project Structure

```
AutoML-MLOps-PipeLine/
├── src/mlpipeline/          # Core pipeline components
├── app/                      # FastAPI application
├── config/                   # Configuration files
├── scripts/                  # Utility scripts
├── airflow/                  # Airflow DAGs
├── monitoring/               # Monitoring components
├── observability/            # Prometheus/Grafana configs
├── notebooks/                # Jupyter notebooks
├── Dockerfile                # Container definition
├── docker-compose.yaml       # Multi-service orchestration
└── requirements.txt          # Python dependencies
```

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