https://github.com/ani717/xgboost-mlops-pipeline
An end-to-end machine learning pipeline using XGBoost trained on the sklearn Breast Cancer dataset. This project demonstrates a full production workflow.
https://github.com/ani717/xgboost-mlops-pipeline
docker-image fastapi-framework kubernetes-deployment logging mlflow pytest requestid validation xgboost-classifier
Last synced: about 1 month ago
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An end-to-end machine learning pipeline using XGBoost trained on the sklearn Breast Cancer dataset. This project demonstrates a full production workflow.
- Host: GitHub
- URL: https://github.com/ani717/xgboost-mlops-pipeline
- Owner: ANI717
- License: mit
- Created: 2025-07-04T19:59:14.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-07-13T04:56:06.000Z (11 months ago)
- Last Synced: 2025-07-13T05:29:09.188Z (11 months ago)
- Topics: docker-image, fastapi-framework, kubernetes-deployment, logging, mlflow, pytest, requestid, validation, xgboost-classifier
- Language: Jupyter Notebook
- Homepage:
- Size: 123 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🚀 XGBoost MLOps Pipeline
A complete end-to-end machine learning pipeline built using **XGBoost**, demonstrating model development, packaging, API serving, and deployment to Kubernetes.
---
## 📌 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
---
## 📁 Project Structure
- [`dev/`](./dev) – Model development and training with MLflow
- [`model/`](./model) – Packaging trained model into a Python wheel
- [`api/`](./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