Amazon-SageMaker-AutoPilot-Workshop

Setup instructions

• Log out of the AWS console if you're already logged in.

• Go to https://dashboard.eventengine.run

• Go to: http://bit.ly/383FEO4 and choose a random non claimed line with a hash. Write your name on the second column.

• Enter the hash that was provided to you, and click on "Accept terms & login"

• Click on "AWS console"

• Click on "Open AWS console"

• Make sure you're using the US East (N. Virginia) - us-east-1 region. Do not use another region.

• Select "Amazon SageMaker" in the search box

• Go to "Notebook / Notebook instances".

• Click on "Create notebook instance".

• "Notebook instance name": type a name for your instance, e.g "sagemaker-autopilot-workshop".

• "Notebook instance type": select ml.t3.medium. No need for anything bigger.

• "IAM role": select "Create a new role"

  • Select "Any S3 bucket".
  • Click on "Create role".

• In the "Git repositories" section:

  • Select "Clone a public Git repository" from the dropdown list.
  • In the "Git repository" box, enter: https://github.com/eitansela/Amazon-SageMaker-AutoPilot-Workshop
  • Click the "Add additional repository" link.
  • Select "Clone a public Git repository" from the dropdown list.
  • In the "Git repository" box, enter: https://github.com/gilinachum/ml-workflows-step-functions

• Click on "Create notebook instance", and wait until the instance is "In Service"

• Click on "Open Jupyter"

• Click on the "sagemaker_autopilot_direct_marketing_lab.ipynb" notebook and get to work :)

Blog post

https://aws.amazon.com/blogs/aws/amazon-sagemaker-autopilot-fully-managed-automatic-machine-learning/

Direct Marketing with Amazon SageMaker XGBoost and Hyperparameter Tuning

https://gitlab.com/juliensimon/aim361/blob/master/Lab1.ipynb