Task-Specific Models

JumpStart supports task-specific models across fifteen of the most popular problem types. Of the supported problem types, Vision and NLP-related types total thirteen. There are eight problem types that support incremental training and fine-tuning. For more information about incremental training and hyper-parameter tuning, see SageMaker AI Automatic Model Tuning.​ JumpStart also supports four popular algorithms for tabular data modeling.

You can search and browse models from the JumpStart landing page in Studio or Studio Classic. When you select a model, the model detail page provides information about the model, and you can train and deploy your model in a few steps. The description section describes what you can do with the model, the expected types of inputs and outputs, and the data type needed for fine-tuning your model.

You can also programmatically utilize models with the SageMaker Python SDK. To list all available models, use list_jumpstart_models() from the SDK:

from sagemaker.core.jumpstart.notebook_utils import list_jumpstart_models

# List every available JumpStart model ID.
model_ids = list_jumpstart_models()
print(model_ids)

This prints a list of model ID strings, such as ['huggingface-llm-falcon-7b', ...].

The list of problem types and links to their example Jupyter notebooks are summarized in the following table.