Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning.
JumpStart also offers example notebooks that use Amazon SageMaker features like spot instance training and experiments over a large variety of model types and use cases. These example notebooks contain code that shows how to apply ML solutions by using SageMaker and JumpStart. They can be adapted to match to your own needs and can thus speed up application development.
Recently, we added 10 new notebooks to JumpStart in Amazon SageMaker Studio. This post focuses on these new notebooks. As of this writing, JumpStart offers 56 notebooks, ranging from using state-of-the-art natural language processing (NLP) models to fixing bias in datasets when training models.
The 10 new notebooks can help you in the following ways: