[Proposal]: Intro to AWS SageMaker for Predictive ML/AI

[qualiaMachine]

1. Lesson Topic

This workshop introduces you to foundational workflows in Amazon SageMaker, covering data setup, code repo setup, model training, and hyperparameter tuning within AWS’s managed environment. You’ll learn how to use SageMaker notebooks to control data pipelines, manage training and tuning jobs, and evaluate model performance effectively. We’ll also cover strategies to help you scale training and tuning efficiently, with guidance on choosing between CPUs and GPUs, as well as when to consider parallelized workflows (i.e., using multiple instances).

2. Lesson Language

English

3. Draft materials

https://uw-madison-datascience.github.io/ML_with_Amazon_SageMaker/

4. Requirements for existing materials

• do your lesson materials conform to our Code of Conduct?
• are your materials already on GitHub and do they use The Carpentries lesson template or The Carpentries Workbench? (you can visit the Workbench tutorial to learn more about how to use our lesson infrastructure).

5. New repository creation

• I need a new repository created

6. Transfer existing repository

• my lesson meets both criteria in part 4 and I would like to transfer the repository to The Carpentries Incubator

7. Collaborators

No response

8. Info/Questions

The current materials are directed towards attendees of an annual ML/AI kaggle hackathon: https://hub.datascience.wisc.edu/2024-machine-learning-marathon/

There are a couple of quirks in the materials because of this (e.g., when creating notebook, we have them select a pre-configured policy). General learners will have to setup their own policy — I plan to update the setup with instructions on creating a policy, so that anyone can follow along.

Cost estimate
Running through this workshop should cost approximately $10-$15 on AWS, assuming moderate usage of GPU instances and a few parallel jobs. For new AWS accounts, the AWS Free Tier may cover some of these costs, including 250 hours per month of the ml.t2.medium instance for the first two months, as well as some limited S3 storage. This means new users may be able to complete certain parts of the workshop for free or at a reduced cost. We recommend monitoring usage through the AWS Billing Dashboard to stay within the free tier and manage any extra expenses effectively.

[tobyhodges]

Hi @qualiaMachine!

I have added you to a Team of maintainers for the lesson, as part of the carpentries-incubator organization on GitHub. Please initiate the transfer into carpentries-incubator, via the repository Settings. When that is done, I will make sure that you retain the appropriate level of access to the repository: you will be given full administrative rights, and the maintainer team will get 'Maintain' access so you can add more maintainers in the future.

I will leave this issue open until the transfer has been completed. If you have any questions about the process, or if you run into any trouble, please post back here and I will be happy to help you.

[qualiaMachine]

Hi @tobyhodges , I have just completed the transfer 👍