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

Hyperparameter Tuning Job Created Using Amazon SageMaker
Imagine you are a data engineer, and your company just selected AWS as the preferred cloud provider. You have been asked to find the optimal set of hyperparameters for the linear learner model by leveraging SageMaker's Automatic Model Tuning (AMT). 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 an S3 bucket. Then the Sagemaker estimator is configured to run four tuning jobs, and will identify the job that minimizes the objective metric.

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.
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Challenge
Install dependencies and import the libraries
- Load required libraries.
- Fetch the dataset from sklearn library.
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Challenge
Download the data and upload it to an S3 bucket
- Convert the downloaded data to a CSV format
- Use the
upload_file
function and upload the CSV files to an 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
Initialize the estimator
- Fetch the linear learner image according to the region.
- Set up the estimator function.
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Challenge
Tune hyperparameters
- Define the hyperparameter ranges.
- Invoke the tuner job.
- Monitor the tuning process from the management console.
About the author
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