sebiwtt / delta

MLOps for online machine learning using Docker and Python

Delta🌊🔀

DeltaMLOps is an innovative tool designed for the efficient deployment and management of online machine learning models, specifically tailored for integration with the River library. It utilizes a microservice architecture, allowing for dynamic and scalable operations in machine learning workflows.

Features 🚀

• Microservice-Based: Each ML model functions as an independent microservice.
• Dual API System: Includes a public API for training and inference, and a management API for configuration and monitoring.
• Dynamic Model Configuration: Easily configurable models at runtime, adaptable to changing data streams.
• Containerized Deployment: Utilizes Docker for consistent and easy deployment, with Kubernetes support for scaling.

Getting Started

Prerequisites

• Docker
• Python 3.8+
• River 0.21.0

Installation

Quickstart

# Quickstart guide with code snippets

Usage

# Example usage code

Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/yourFeature)
3. Commit your Changes (git commit -m 'Add some Feature')
4. Push to the Branch (git push origin feature/yourFeature)
5. Open a Pull Request

License

Distributed under the BSD License. See LICENSE for more information.

Contact

Sebastian Wette - [email protected]