NCA-GENM Question 172
Select 2You are assisting a senior team member in optimizing the training process of a multimodal generative AI model. The model is underperforming, and the senior team member has asked you to focus on minimizing training time without sacrificing model accuracy. Which of the following actions should you take under their supervision?
- A
Implement mixed precision training to reduce memory usage and improve computation speed.
- B
Decrease the size of the training dataset to reduce the training time.
- C
Use a learning rate scheduler to dynamically adjust the learning rate during training.
- D
Enable data augmentation techniques to artificially expand the dataset.
- E
Switch from a pre-trained model to training a model from scratch for better control over the architecture.
Show answer and explanation
Correct answers: A, C
Explanation
To optimize training time without sacrificing accuracy, efficient methods like mixed precision training and learning rate scheduling can be employed. Mixed precision training accelerates computations, while a learning rate scheduler improves convergence. Other options, such as reducing the dataset size or training from scratch, either compromise accuracy or are counterproductive to the goal of minimizing training time.
- A. Correct.
Mixed precision training leverages lower precision (e.g., FP16) for faster computation and reduced memory usage while maintaining acceptable accuracy, making it a valid optimization technique.
- B. Incorrect.
Reducing the dataset size might minimize training time but would likely sacrifice model accuracy, which directly contradicts the requirement to maintain accuracy.
- C. Correct.
A learning rate scheduler optimizes training by dynamically adjusting the learning rate, improving convergence speed and stability, making it an effective method for reducing training time while maintaining accuracy.
- D. Incorrect.
While data augmentation can improve model robustness, it increases the size of the dataset, which could lead to longer training times, contradicting the goal of minimizing training duration.
- E. Incorrect.
Training a model from scratch is computationally expensive and time-consuming. It does not align with the goal of optimizing training time, especially when pre-trained models can significantly accelerate the process.