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Using Amazon S3 as a Machine Learning Repository

Imagine you are a starting Data Engineer. You have been tasked with preparing an environment for model building. In order to complete this task you need to ingest a csv file into S3 and then load that data source into a Jupyter Notebook. Finally you need to save that data back into S3 under a different table.

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Path Info

Level
Clock icon Advanced
Duration
Clock icon 30m
Published
Clock icon May 24, 2024

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Table of Contents

  1. Challenge

    Prepare the Environment

    1. Create a Jupyter Notebook in SageMaker Create a new role that allows for interaction with S3
    2. Create an S3 Bucket
    3. Download Parking-Ticket-2022 data (https://open.toronto.ca/dataset/parking-tickets/)
    4. Upload file: Parking_Tags_Data_2022.000.csv to the S3 Bucket

  2. Challenge

    Ingest Data Into SageMaker

    1. Create a conda_python3 Jupyter Notebook
    2. Ingest data from the S3 instance into the Jupyter Notebook as a data frame.
    3. Confirm that you can see 5 rows of the Parking_Tags_Data table
    4. Change the df.head command to display 11 rows instead of 5.
    5. Drop the 'location3' column from the data frame
    6. Write that table back into S3 as a csv file named 'Result.csv'
    7. Verify the result in S3.
A Cloud Guru - Labs - Using Amazon S3 as a Machine Learning Repository
Author
A Cloud Guru

The Cloud Content team comprises subject matter experts hyper focused on services offered by the leading cloud vendors (AWS, GCP, and Azure), as well as cloud-related technologies such as Linux and DevOps. The team is thrilled to share their knowledge to help you build modern tech solutions from the ground up, secure and optimize your environments, and so much more!

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