Hyperparameter Tuning with Amazon SageMaker's Automatic Model Tuning


ON-DEMAND


Hyperparameter Tuning with Amazon SageMaker's Automatic Model Tuning



Broadcast Date:
July 24, 2018

Level 300 | Service Deep Dive
Learn how to use Automatic Model Tuning with Amazon SageMaker to get the best machine learning model for your dataset. Training machine models requires choosing seemingly arbitrary hyperparameters like learning rate and regularization to control the learning algorithm. Traditionally, finding the best values for the hyperparameters requires manual trial-and-error experimentation. Amazon SageMaker makes it easy to get the best possible outcomes for your machine learning models by providing an option to create hyperparameter tuning jobs. These jobs automatically search over ranges of hyperparameters to find the best values. Using sophisticated Bayesian optimization, a meta-model is built to accurately predict the quality of your trained model from the hyperparameters.

Learning Objectives:
• Understand what hyperparameters are and what they do for training machine learning models
• Learn how to use Automatic Model Tuning with Amazon SageMaker for creating hyperparameter tuning of your training jobs
• Strategies for choosing and iterating on tuning ranges of a hyperparameter tuning job with Amazon SageMaker

Suited For: Developers/Data Scientists with a good working knowledge of Machine Learning

Speaker(s): Leo Dirac, Principal Engineer, AWS


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