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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.

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Labs

Path Info

Level
Clock icon Intermediate
Duration
Clock icon 1h 0m
Published
Clock icon Apr 12, 2024

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Table of Contents

  1. Challenge

    Launch SageMaker Notebook

    1. Log in to the AWS console and navigate to AWS SageMaker.
    2. Load the Jupyter Notebook that has been provided with this hands-on lab.
  2. Challenge

    Install dependencies and import the libraries

    1. Load required libraries.
    2. Fetch the dataset from sklearn library.
  3. Challenge

    Download the data and upload it to an S3 bucket

    1. Convert the downloaded data to a CSV format
    2. Use the upload_file function and upload the CSV files to an S3 bucket
  4. Challenge

    Set up training and validation data

    1. Create the inputs for the fit() function with the training and validation location.
  5. Challenge

    Initialize the estimator

    1. Fetch the linear learner image according to the region.
    2. Set up the estimator function.
  6. Challenge

    Tune hyperparameters

    1. Define the hyperparameter ranges.
    2. Invoke the tuner job.
    3. Monitor the tuning process from the management console.

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