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Cloud ML Pipeline Framework
A scalable framework for building and deploying machine learning pipelines in the cloud
Cloud ML Pipeline Framework
A production-ready framework for building, training, and deploying machine learning models at scale.
Overview
This project provides a comprehensive solution for managing the entire ML lifecycle in cloud environments. It includes tools for data preprocessing, model training, evaluation, and deployment.
Key Features
- Scalable Architecture: Built to handle large-scale data processing
- Cloud-Native: Designed for AWS, GCP, and Azure
- MLOps Ready: Integrated monitoring and versioning
- Flexible: Supports various ML frameworks (TensorFlow, PyTorch, scikit-learn)
Tech Stack
- Python 3.9+
- Docker & Kubernetes
- Apache Airflow
- MLflow
- Cloud provider SDKs
Getting Started
Check out the GitHub repository for installation instructions and documentation.
Want to Contribute?
This project is open source. Contributions, issues, and feature requests are welcome!