This section of the documentation introduces the main concepts of UnionML and aims to show you the core pieces that together make up a UnionML app.
A UnionML app is composed of two objects:
Model. Together, they expose method decorator entrypoints that serve as the core
building blocks of an end-to-end machine learning application. The following sections will show you how to:
Define a Dataset: specify where to read data from and how to split it into training and test sets, parse out features and targets, and iterate through batches of data.
Train and Predict Locally: perform training and prediction using a UnionML as a regular python script, then serve a
FastAPIapp for prediction.