Amazon SageMaker JumpStart helps you quickly and easily get started with ML by providing access to hundreds of built-in algorithms with pretrained models from popular model hubs through the user interface. Using the SageMaker Python SDK, you can select a prebuilt model from the model zoo to train on custom data or deploy to a SageMaker endpoint for inference. To make it easier to get started, SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey.
What you will accomplish
In this guide, you will:
- Deploy a SageMaker JumpStart pretrained model
- Run inferences using the endpoint deployed from SageMaker JumpStart
Before starting this guide, you will need:
- An AWS account: If you don't already have an account, follow the Setting Up Your AWS Environment getting started guide for a quick overview.
For this tutorial, you will deploy a model called BERT Base Cased that has been pretrained on English text using Wikipedia and that performs well on text classification use cases.
Choose the AWS CloudFormation stack link. This link opens the AWS CloudFormation console and creates your SageMaker Studio domain and a user named studio-user. It also adds the required permissions to your SageMaker Studio account. In the CloudFormation console, confirm that US East (N. Virginia) is the Region displayed in the upper right corner. Stack name should be CFN-SM-IM-Lambda-catalog, and should not be changed. This stack takes about 10 minutes to create all the resources.
This stack assumes that you already have a public VPC set up in your account. If you do not have a public VPC, see VPC with a single public subnet to learn how to create a public VPC.
Select I acknowledge that AWS CloudFormation might create IAM resources, and then choose Create stack.
On the CloudFormation pane, choose Stacks. When the stack is created, the status of the stack should change from CREATE_IN_PROGRESS to CREATE_COMPLETE.
Enter SageMaker Studio into the CloudFormation console search bar, and then choose SageMaker Studio.
Choose US East (N. Virginia) from the Region dropdown list on the upper right corner of the SageMaker console. For Launch app, select Studio to open SageMaker Studio using the studio-user profile.
You have learned how to select and deploy an Amazon SageMaker JumpStart pretrained model and make predictions.