
This post explores advanced techniques for hyperparameter optimization using Amazon SageMaker Automatic Model Tuning, including using custom training code with Scikit-learn, defining custom evaluation metrics, applying different HPO strategies like random search and Bayesian optimization, visualizing and comparing tuning results, and utilizing warm starts to iteratively refine the hyperparameter space.


<div>
<p>This article explores advanced techniques for hyperparameter optimization with Amazon SageMaker Automatic Model Tuning (AMT). It demonstrates how to build a digit recognition model from scratch using scikit-learn, perform hyperparameter optimization (HPO), and visualize the results.</p>
<p>Specifically, the article covers:</p>
<ul>
<li>Using custom training code and Scikit-learn in SageMaker Training</li>
<li>Defining custom evaluation metrics based on logs</li>
<li>Performing HPO using random search and Bayesian optimization strategies</li>
<li>Visualizing and comparing tuning results with an interactive solution</li>
<li>Continuing exploration of the hyperparameter space using warm start HPO jobs</li>
<li>Conclusion on the benefits of AMT for efficient hyperparameter tuning</li>
</ul>
</div>


Explore advanced techniques for hyperparameter optimization with Amazon SageMaker Automatic Model Tuning

Related articles