Getting started: Models

Deploy, manage, and invoke your models on robust production infrastructure.
A well-trained ML model feels like magic to use, but what good is that magic if it stays shut in your spellbook ... er, Jupyter notebook? Baseten isn't just for building full-stack applications, it's for building ML-powered full-stack applications, which begins with deploying powerful models on robust infrastructure and accessing them via an API.
Deploying and managing models
Workspaces on the free Personal plan are limited to a single deployed model. Pre-trained models provided by Baseten do not count against this limit. To increase the number of models for your workspace, upgrade to a paid Baseten plan.
Models are trained using heterogeneous technologies and methods, so there are many ways to deploy a model to Baseten, including pre-trained models, models you've trained locally, and models from SageMaker. Once you have deployed your model, you can monitor its health, resources, and logs.