Niels here. I'm the creator of UnionML (https://github.com/unionai-oss/unionml), a Python MLOps framework that removes the boilerplate and friction associated with building and deploying machine learning systems to production.

I've been training and deploying models for almost a decade now, and one pain-point I've consistently had is managing the complexity of building and maintaining an ML stack that works for the entire model development lifecycle - from prototyping to production.

UnionML is built on top of Flyte (https://www.flyte.org) and exposes a functional interface for defining the building blocks of your ML application via decorators -- think Flask or FastAPI method endpoints -- and UnionML takes care of bundling them into microservices for different use cases such as:

- model training

- batch prediction

- online prediction

- (more coming soon!)

This project aims to unify the rich ecosystem of data, ML, and MLOps tools that have emerged over the last decade or so (e.g. MLFlow, Sagemaker, Spark, etc.) to provide a nice UX for model developers, in both individual and team settings.