This sample project demonstrates using SageMaker AI to tune the hyperparameters of a machine learning model, and to batch transform a test dataset.
In this project, Step Functions uses a Lambda function to seed an Amazon S3 bucket with a test dataset. It then creates a hyperparameter tuning job using the SageMaker AI service integration. It then uses a Lambda function to extract the data path, saves the tuning model, extracts the model name, and then runs a batch transform job to perform inference in SageMaker AI.
For more information about SageMaker AI and Step Functions service integrations, see the following:
Note
This sample project may incur charges.
For new AWS users, a free usage tier is available. On this tier, services are free
below a certain level of usage. For more information about AWS costs and the Free
Tier, see SageMaker AI
Pricing
Step 1: Create the state machine
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Open the Step Functions console
and choose Create state machine. -
Choose Create from template and find the related starter template. Choose Next to continue.
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Choose how to use the template:
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Run a demo – creates a read-only state machine. After review, you can create the workflow and all related resources.
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Build on it – provides an editable workflow definition that you can review, customize, and deploy with your own resources. (Related resources, such as functions or queues, will not be created automatically.)
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Choose Use template to continue with your selection.
Note
Standard charges apply for services deployed to your account.
Step 2: Run the demo state machine
If you chose the Run a demo option, all related resources will be deployed and ready to run. If you chose the Build on it option, you might need to set placeholder values and create additional resources before you can run your custom workflow.
Choose Deploy and run.
Wait for the AWS CloudFormation stack to deploy. This can take up to 10 minutes.
After the Start execution option appears, review the Input and choose Start execution.
Congratulations!
You should now have a running demo of your state machine. You can choose states in the Graph view to review input, output, variables, definition, and events.
