Deploy a pre-trained model to kickstart your ML-powered application.
Baseten provides a growing set of pre-trained models that solve common ML tasks. These models are a great way to kickstart your ML application and showcase the features and functionality of Baseten - you can deploy pre-trained models along with optional application templates to add ML into your application in minutes.
Pre-trained models offer many of the same features as your own models deployed on Baseten:
- You can invoke the model directly through the Baseten Python client or an API call
- You can create an application for the model
- You can use the model in worklets
- They come with editable readmes
Pre-trained models have some limitations that your models do not have:
- You can't view health metrics or logs for pre-trained models
- Pre-trained models do not have multiple versions
- Pre-trained models have fixed model resources and auto-scaling settings
- Pre-trained models do not count against the models limit in your workspace
To deploy a pre-trained model, head to the Explore tab. Scroll through pre-trained models, filter by tags, or search for the model you want to deploy. Click on that model, then click "Deploy on Baseten" to deploy it.
Deploying a pre-trained model
By default, every pre-trained model comes with a starter application demonstrating how to use the model for a common use case. When you're in the deployment modal, you can opt out of creating this starter application by toggling off the "Create a starter application" option.
Once the model is deployed, you can edit the starter app or use the model in your applications.
Pre-trained models can be applied to common ML tasks. From speech transcription to sentiment analysis, image classification to photo restoration, pre-trained models deliver powerful ML capabilities directly to your applications. Baseten currently offers 24 pre-trained models: