All 7 Amazon SageMaker & Machine Learning on AWS questions
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What is SageMaker hyperparameter tuning, and how does it help improve model accuracy?

Intermediate

Answer

SageMaker automatic hyperparameter tuning, also called hyperparameter optimization, runs multiple training jobs automatically with different combinations of hyperparameter values, such as learning rate or the number of trees in a decision forest, using intelligent search strategies to converge on the combination that produces the best performing model according to a metric you define, saving significant manual trial and error effort.
aws sagemaker create-hyper-parameter-tuning-job --hyper-parameter-tuning-job-name my-tuning-job --hyper-parameter-tuning-job-config file://tuning-config.json --training-job-definition file://training-definition.json
Real-world example A data scientist configures a SageMaker tuning job to automatically test dozens of different combinations of learning rate and tree depth for a fraud detection model, ultimately identifying a configuration that significantly outperforms their initial manual guess.

Common follow-ups: What search strategies does SageMaker use for hyperparameter tuning?;How many parallel training jobs can a tuning job run at once?

AWS Batch;Monitoring (CloudWatch)
All 7 Amazon SageMaker & Machine Learning on AWS questions