You work as a machine learning specialist on a team tasked with designing an image recognition system that can quickly adapt to new observations. Your team is designing automated driving software for cars in a ride-share fleet. Your company wants to implement a service where when users hail a ride through your app on their mobile device. A nearby self-driving car arrives at the user’s location. It has the desired route preloaded and is ready to take the user to their destination. Your team has decided to use the SageMaker Image Classification algorithm in your image recognition model.
The machine learning models powering this self-driving car fleet need to react very quickly to new observations, such as previously not encountered obstacles like different types and sized animals, etc. Which hyperparameter would you set, and to what value, to obtain the desired outcome?

Answer options:

A.early_stopping set to True
B.early_stopping set to False
C.learning_rate set to 0.1
D.learning_rate set to 0.8
E.use_pretrained_model set to 0
F.use_pretrained_model set to 1

Answer: D
Option A is incorrect. The early_stopping hyperparameter is used to decide whether to use early stopping during training. This hyperparameter allows you to terminate a training job early if it is observed that further training will not be necessary. Tuning this hyperparameter would not help your model react very quickly to new observations.
Option B is incorrect. The early_stopping hyperparameter is used to decide whether to use early stopping during training. This hyperparameter allows you to terminate a training job early if it is observed that further training will not be necessary. Tuning this hyperparameter would not help your model react very quickly to new observations.
Option C is incorrect. The learning_rate hyperparameter governs how quickly the model adapts to new or changing data. Valid values range from 0.0 to 1.0. Setting this hyperparameter to a low value, such as 0.1, will make the model learn more slowly. This is not what you want. You want your model to learn very rapidly.
Option D is correct. The learning_rate hyperparameter governs how quickly the model adapts to new or changing data. Valid values range from 0.0 to 1.0. Setting this hyperparameter to a high value, such as 0.8, will make the model learn quickly. This is what you want. You want your model to learn very rapidly.
Option E is incorrect. The use_pretrained_model hyperparameter defines whether you want a pre-trained model to be loaded before training. This will not help you adapt quickly to new or changing observations.
Option F is incorrect. The use_pretrained_model hyperparameter defines whether you want a pre-trained model to be loaded before training. This will not help you adapt quickly to new or changing observations.
Reference:
Please see the Amazon SageMaker developer guide titled Image Classification Hyperparameters, and the Amazon Machine Learning blog titled Amazon SageMaker Automatic Model Tuning now supports early stopping of training jobs.