Intro to AWS SageMaker for Predictive ML/AI
This introductory AWS SageMaker workshop teaches core workflows for running predictive ML/AI models in AWS SageMaker, an AWS-managed machine learning environment. Participants will learn to set up data, configure SageMaker Notebooks, manage code repositories, train and tune models, and optimize resource costs effectively within AWS. Users will benefit from tips on controlling AWS expenses and scaling models efficiently, with real-world guidance on choosing appropriate CPU and GPU resources.
A personal AWS account is fine for this workshop. However, for long-term research use, we recommend switching to a UW-provisioned AWS account. You’ll get institutional pricing via Internet2 NET+, lower overhead on grants (26% instead of 55.5% — saving ~$2,950 per $10k in cloud costs), data protection agreements (including BAA for HIPAA), and dedicated support from the Public Cloud Team. NIH-funded researchers can get additional discounts through the STRIDES Initiative.
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Cost estimate
Running through this workshop should cost approximately $5-$10 on AWS, assuming moderate usage of GPU instances and a few parallel jobs (i.e., sticking to the lesson materials). 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. New users may be able to complete certain parts of the workshop for free or at a significantly reduced cost. We recommend monitoring usage through the AWS Billing Dashboard to stay within the free tier and manage any extra expenses effectively.
Prerequisites
Estimated time to complete
3-5 hours: Based on running through training, tuning, and experimenting with example code setups.
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