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.
- 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.
- Docker
- Python 3.8+
- River 0.21.0
# Quickstart guide with code snippets# Example usage codeContributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/yourFeature) - Commit your Changes (
git commit -m 'Add some Feature') - Push to the Branch (
git push origin feature/yourFeature) - Open a Pull Request
Distributed under the BSD License. See LICENSE for more information.
Sebastian Wette - mail@sebastianwette.de