Amazon Web Services AWS Certified Specialty MLS-C01 Question # 83 Topic 9 Discussion

Using log-scaled hyperparameters is a guideline that can improve the automatic model tuning in Amazon SageMaker. Log-scaled hyperparameters are hyperparameters that have values that span several orders of magnitude, such as learning rate, regularization parameter, or number of hidden units. Log-scaled hyperparameters can be specified by using a log-uniform distribution, which assigns equal probability to each order of magnitude within a range. For example, a log-uniform distribution between 0.001 and 1000 can sample values such as 0.001, 0.01, 0.1, 1, 10, 100, or 1000 with equal probability. Using log-scaled hyperparameters can allow the hyperparameter optimization feature to search the hyperparameter space more efficiently and effectively, as it can explore different scales of values and avoid sampling values that are too small or too large. Using log-scaled hyperparameters can also help avoid numerical issues, such as underflow or overflow, that may occur when using linear-scaled hyperparameters. Using log-scaled hyperparameters can be done by setting the ScalingType parameter to Logarithmic when defining the hyperparameter ranges in Amazon SageMaker12

The other options are not valid or relevant guidelines for improving the automatic model tuning in Amazon SageMaker. Choosing the maximum number of hyperparameters supported by Amazon SageMaker to search the largest number of combinations possible is not a good practice, as it can increase the time and cost of the tuning job and make it harder to find the optimal values. Amazon SageMaker supports up to 20 hyperparameters for tuning, but it is recommended to choose only the most important and influential hyperparameters for the model and algorithm, and use default or fixed values for the rest3 Specifying a very large hyperparameter range to allow Amazon SageMaker to cover every possible value is not a good practice, as it can result in sampling values that are irrelevant or impractical for the model and algorithm, and waste the tuning budget. It is recommended to specify a reasonable and realistic hyperparameter range based on the prior knowledge and experience of the model and algorithm, and use the results of the tuning job to refine the range if needed4 Executing only one hyperparameter tuning job at a time and improving tuning through successive rounds of experiments is not a good practice, as it can limit the exploration and exploitation of the hyperparameter space and make the tuning process slower and less efficient. It is recommended to use parallelism and concurrency to run multiple training jobs simultaneously and leverage the Bayesian optimization algorithm that Amazon SageMaker uses to guide the search for the best hyperparameter values5