👨 Guide for users

Ruby AI platform lets you run machine learning models with a cloud API, without having to understand the intricacies of machine learning or manage your own infrastructure. You can run open-source models that other people have published, or package and publish your own models. Those models can be public or private.

Terminology

Let's start by defining some important terms that you'll need to know:

Models: In the world of machine learning, the word "model" can mean many different things depending on context. It can be the source code, the trained weights, the architecture, or some combination thereof. At Ruby AI, when we say "model" we're generally referring to a trained, packaged, and published software program that accepts inputs and returns outputs.

Versions: Just like normal software, machine learning models change and improve over time, and those changes are released as new versions. Whenever a model author retrains a model with new data, fixes a bug in the source code, or updates a dependency, those changes can influence the behavior of the model. The changes are published as new versions, so model authors can make improvements without disrupting the experience for people using older versions of the model. Versioning is essential to making machine learning reproducible: it helps guarantee that a model will behave consistently regardless of when or where it's being run.