FuseML aims to provide an MLOps framework as the medium dynamically integrating together the AI/ML tools of your choice. It's an extensible tool built through collaboration, where Data Engineers and DevOps Engineers can come together and contribute with reusable integration code.
Build your own custom MLOps orchestration workflows from composable automation recipes adapted to your favorite AI/ML tools, to get you from ML code to inference serving in production as fast as lighting a fuse.
Use FuseML to build a coherent stack of community shared AI/ML tools to run your ML operations. FuseML is powered by a flexible framework designed for consistent operations and a rich collection of integration formulas reflecting real world use cases that help you reduce technical debt and avoid vendor lock-in.
FuseML originated as a fork of our sister open source project Epinio, a lightweight open source PaaS built on top of Kubernetes, then has been gradually transformed and infused with the MLOps concepts that make it the AI/ML orchestration tool that it is today.
The project is under heavy development following the main directions:
Take a look at our Project Board to see what we're working on and what's in store for the next release.
The basic FuseML workflow can be described as an MLOps type of workflow that starts with your ML code and automatically runs all the steps necessary to build and serve your machine learning model. FuseML's job begins when your machine learning code is ready for execution.
This repository contains the code for the FuseML installer and is the main project repository. Other repositories of interest are: