Currently, UnionML apps support two core types of machine learning microservices: model training and model serving.
Production Training and Batch Predictions#
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
Deploy to a Flyte Cluster: Deploy training and prediction services to a Flyte cluster.
Serving Online Predictions#
Once you have a trained model object that you want to serve in production, you can:
Serving Reactive Predictions#
Some predictive applications require reacting to events that occur in some external system:
Reacting to S3 Events: Generate predictions in response to files being dumped into a specified S3 path.