Google Professional Machine Learning Engineer Question 228
Select 3Google Cloud PlatformYou are training a machine learning model on Google Cloud to predict customer churn for a subscription-based service. The dataset contains over 10 million rows with features such as customer demographics, usage patterns, and subscription history. The training process is slow, and you notice that the model is overfitting on the training data. Which actions should you take to address these challenges and optimize the training process?
- A
Reduce the size of the dataset by randomly sampling a smaller subset for training.
- B
Implement early stopping during training to prevent overfitting.
- C
Use a distributed training approach with TensorFlow on Vertex AI to speed up training.
- D
Add regularization techniques, such as L2 regularization, to reduce overfitting.
- E
Increase the batch size significantly to improve model generalization.
Show answer and explanation
Correct answers: B, C, D
Explanation
To address the challenges of slow training and overfitting, you should leverage distributed training on Google Cloud to handle the large dataset efficiently. Overfitting can be mitigated by implementing early stopping and regularization techniques. These actions ensure the model trains faster while improving its ability to generalize to unseen data.
- A. Incorrect.
Reducing the size of the dataset may speed up training but can cause the model to lose important information, potentially leading to poor generalization.
- B. Correct.
Early stopping is an effective way to monitor the model's performance on a validation set and stop training when performance starts to degrade, helping to address overfitting.
- C. Correct.
Distributed training allows the training process to leverage multiple machines or GPUs, significantly improving speed for large datasets like this one.
- D. Correct.
Regularization techniques, such as L2 regularization, help reduce overfitting by penalizing overly complex models, improving generalization.
- E. Incorrect.
Increasing the batch size may improve training speed but does not directly address overfitting. In some cases, very large batch sizes can even harm model generalization.