Training Reports Utilized in SageMaker Debugger to Improve Your Models

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Training Reports Utilized in SageMaker Debugger to Improve Your Models

Imagine you are the data engineer at your company, and your company has just selected AWS as the preferred cloud provider. You have been given a dataset to predict if an individual makes more than $50K in salary. As part of the modeling process, you have been asked to generate a summary of the model training evaluation results, insights into the model performance, and interactive graphs. In this lab, you will fetch the census data and use that as the input dataset. Once the data is split, the data is uploaded to the S3 bucket. Then, the Sagemaker estimator is configured with the debugger hook and Sagemaker built-in rules to generate performance metric reports.

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Lab Info

Level

Intermediate

Last updated

Sep 24, 2025

Duration

1h 0m

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.
Challenge

Install dependencies and import the libraries
1. Load required libraries.
2. Fetch the census dataset from shap library.
Challenge

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

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

Configure and run the estimator
1. Fetch the xgboost image according to the region.
2. Setup the estimator function with the debugger hook and debugger built-in rules.
3. Initiate the training process.
Challenge

View the generated reports
1. Fetch the training and profile reports from S3 bucket.
2. Review the graphs generated by the training report.
3. Understand the resource utilization statistics generated by the profiling report.

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