Google Professional Machine Learning Engineer Question 106
Select 3Google Cloud PlatformYou are developing a machine learning model for a large dataset on Google Cloud. The initial training process is taking significantly longer than expected due to the dataset size and model complexity. Which of the following strategies should you implement to optimize the training process while maintaining model accuracy?
- A
Leverage TensorFlow's tf.data API to preprocess and load the data efficiently in batches.
- B
Use a smaller subset of the dataset to train the model and skip the full dataset training.
- C
Enable GPU or TPU acceleration to speed up the training computation.
- D
Use pre-trained embeddings or transfer learning to reduce computational requirements.
- E
Increase the batch size significantly to process more data in each training step.
Show answer and explanation
Correct answers: A, C, D
Explanation
Efficient model training involves optimizing data pipelines, leveraging specialized hardware like GPUs or TPUs, and applying techniques such as transfer learning to reduce computation time. These strategies ensure training is efficient without compromising the model's accuracy. Using a smaller dataset or excessively large batch sizes may result in reduced model performance or other unintended consequences.
- A. Correct.
Using TensorFlow's tf.data API enables efficient data pipelines for preprocessing and loading, reducing I/O bottlenecks and improving training efficiency.
- B. Incorrect.
Using a smaller subset of the dataset could lead to faster training but risks underfitting and a loss in model accuracy, making it unsuitable in most cases.
- C. Correct.
Enabling GPU or TPU acceleration can significantly speed up training by using hardware optimized for parallel computation.
- D. Correct.
Using pre-trained embeddings or transfer learning allows the model to leverage pre-computed features, reducing both the training time and computational requirements.
- E. Incorrect.
While increasing the batch size can speed up training, excessively large batch sizes may lead to memory constraints and slower convergence, making it a less reliable strategy.