Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 44 discussion

Increase the volume of data that is used in training: If the model performs well on the training dataset but poorly on production data, it could be due to overfitting or the model not generalizing well. Increasing the volume of data can help the model generalize better to unseen data and improve its robustness, thus improving performance in production.

The company should use hyperparameters for model tuning, which involves adjusting parameters such as regularization, learning rates, and dropout rates to enhance the model's ability to generalize well to new data Explanation: Hyperparameter tuning is the most effective solution in this scenario because it allows the company to adjust the settings that control the learning process of the model. By fine-tuning hyperparameters, such as increasing regularization or early stopping or adjusting dropout rates, the model can avoid overfitting to the training data and better generalize to new, unseen data in production. This approach helps improve the model's performance across various data distributions.

C: Increase the volume of data that is used in training. Explanation: The issue described is likely caused by overfitting, where the model performs well on the training dataset but fails to generalize to unseen data. Increasing the volume of training data can help mitigate overfitting by providing the model with more diverse examples, improving its ability to generalize to new data in production.

The correct answer is C. Increasing the volume of data used in training can help improve the model's performance in production by providing it with more diverse examples to learn from.

How can you prevent overfitting? • Increase the training data size • Early stopping the training of the model • Data augmentation (to increase diversity in the dataset) • Adjust hyperparameters (but you can’t “add” them)

To prevent overfitting, increase training data, use early stopping, apply data augmentation, and fine-tune hyperparameters without adding new ones.

Reducing the training data make the model prone to overfitting, and will likely further degrade the model's performance.

More diverse training data helps the model learn broader patterns and generalize better to unseen data in production. This reduces the risk of overfitting to the training set. Reduced Overfitting: The significant performance drop in production suggests overfitting to the training data. Increasing the data volume can help the model learn more robust features that are truly predictive rather than memorizing specifics of a limited dataset.. For A - Reducing the training data volume would likely exacerbate the problem rather than solve it. The model's poor performance in production suggests it's not generalizing well, which is often a result of insufficient or non-representative training data.

yes Overfitting.. but if the "Volume Data" is FIXED, meaning if they are going to reuse the same data.. this time the need to REDUCE it.. so "A" if they have MORE/EXTRA data to augment the one already available.. than C

yes Overfitting.. but if the "Volume Data" is FIXED, meaning if they are going to reuse the same data.. this time the need to REDUCE it.. so "A" if they have MORE/EXTRA data to augment the one already available.. than C

Model is overfitting. Needs more training data