- Lab
-
Libraries: If you want this lab, consider one of these libraries.
- AI
- Cloud

Linear Regression Performed Using Amazon SageMaker
Imagine you are the data engineer at your company and your company just selected AWS as the preferred cloud provider. You have been asked to train an ML model using linear learner algorithm. In this lab, you will fetch the iris data and use that as the input dataset. Once the data is split, the data is uploaded to S3 bucket. Then the Sagemaker estimator is configured before initiating the training process.

Lab Info
Table of Contents
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Challenge
Launch SageMaker Notebook
- Log in to the AWS console and navigate to AWS SageMaker.
- Load the Jupyter Notebook that has been provided with this hands-on lab.
-
Challenge
Install dependencies and import the libraries
- Load required libraries.
- Fetch the dataset from sklearn library.
-
Challenge
Download the data and upload them to S3 bucket
- Convert the downloaded data to a CSV format
- Use the
upload_file
function and upload the CSV files to S3 bucket
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Challenge
Set up training and validation data
- Create the inputs for the
fit()
function with the training and validation location.
- Create the inputs for the
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Challenge
Train the model
- Fetch the linear learner image according to the region.
- Setup the estimator function.
- initiate the training process using the
fit()
function.
About the author
Real skill practice before real-world application
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