Deployment Guide

Currently, UnionML apps support two core types of machine learning microservices: model training and model serving.

Production Backend Deployment

UnionML uses flytekit under the hood to execute your training and prediction workflows locally, but you can benefit from the reproducibility and scalability benefits of UnionML by deploying your workflows to a production-grade flyte cluster.

• Deploy to a Flyte Cluster: Deploy training and prediction services to a Flyte cluster.

Serving Predictions

Once you have a trained model object that you want to serve in production, you can:

• Serve with FastAPI: Stand up an online prediction service with FastAPI.

• Serve with AWS Lambda: Create an online prediction serverless endpoint with AWS Lambda.