🚀 XGBoost MLOps Pipeline

A complete end-to-end machine learning pipeline built using XGBoost, demonstrating model development, packaging, API serving, and deployment to Kubernetes.

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📌 Key Components

• 🔬 Model Training with MLflow experiment tracking
• 📦 Model Packaging as a Python wheel for reusability
• ⚡ Model Serving via a FastAPI application
• 🐳 Containerization with Docker
• ☸️ Deployment to a local or cloud Kubernetes cluster

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📁 Project Structure

• dev/ – Model development and training with MLflow
• model/ – Packaging trained model into a Python wheel
• api/ – Serving predictions via FastAPI

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🧠 Tech Stack

• XGBoost for model training
• MLflow for experiment tracking
• FastAPI for serving the model
• Docker for containerization
• Kubernetes for orchestration
• Pytest for testing with coverage
• Logging, RequestID, and Validation middleware for observability