Automatically Create Machine Learning Models
What you will accomplish
In this guide, you will:
- Create a training experiment using SageMaker Autopilot
- Explore the different stages of the training experiment
- Identify and deploy the best performing model from the training experiment
- Predict with your deployed model
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 workflow, you will use a synthetically generated auto insurance claims dataset. The raw inputs are two tables of insurance data: a claims table and a customers table. The claims table has a fraud column indicating whether a claim was fraudulent or otherwise. For the purposes of this tutorial, we have selected a small portion of the dataset. However, you can follow the same steps in this tutorial to process large datasets.
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.
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.
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.
Congratulations! You have now completed the Automatically Create Machine Learning Models tutorial.
You have successfully used SageMaker Autopilot to automatically build, train, and tune models, and then deploy the best candidate model to make predictions.