- Lab
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Using Amazon S3 as a Machine Learning Repository
Machine learning models require data -- and a lot of it! In this lab, you'll explore creating a model building environment for machine learning by setting up an S3 bucket to store data, importing that data into a newly-created SagerMaker AI-backed Jupyter Notebook for processing, then exporting altered data from the Jupyter Notebook back into S3.
Lab Info
Table of Contents
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Challenge
Prepare the Machine Learning Environment
Deploy the needed SageMaker AI Jupyter Notebook for use in processing data, ensuring that the notebook has access to the provided S3 data bucket.
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Challenge
Ingest, Alter, and Export Data with Amazon SageMaker
Use the Jupyter Notebook to ingest and review the provided CSV data. Then, modify the ingested data and practice exporting it into the provided bucket, ensuring it ends up in the appropriate bucket destination.
Real skill practice before real-world application
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