MLA-C01 Question 151
Select 3You are building a machine learning pipeline on AWS to predict customer churn for a subscription-based service. You have pre-processed your data and are now focused on training and refining your model to achieve optimal accuracy. You decide to use Amazon SageMaker for this task. Which steps should you take to train and refine your model effectively?
- A
Choose an appropriate built-in SageMaker algorithm or bring your own algorithm for training.
- B
Use hyperparameter tuning jobs in SageMaker to optimize the model's performance.
- C
Train your model on a small subset of data to reduce costs and skip evaluating the model's performance.
- D
Monitor the model training with SageMaker Debugger to detect potential issues like overfitting or vanishing gradients.
- E
Automatically deploy the model after training without verifying its performance on a validation set.
Show answer and explanation
Correct answers: A, B, D
Explanation
To train and refine your model effectively, you need to select an appropriate algorithm, optimize hyperparameters, and monitor training to detect potential issues. These steps ensure that the model achieves optimal performance while avoiding common pitfalls like overfitting or poor generalization. Skipping validation or prematurely deploying the model can lead to suboptimal results.
- A. Correct.
Correct: Selecting an appropriate algorithm is a crucial first step in training a machine learning model. SageMaker offers built-in algorithms or the flexibility to use custom algorithms.
- B. Correct.
Correct: Hyperparameter tuning helps find the best combination of hyperparameters to improve model performance, making it a key step in refining the model.
- C. Incorrect.
Incorrect: Training a model on a small subset of data may reduce costs but skipping performance evaluation can lead to a poorly generalized model.
- D. Correct.
Correct: SageMaker Debugger provides valuable insights during training, such as identifying overfitting or vanishing gradients, which can help refine the model.
- E. Incorrect.
Incorrect: Automatically deploying a model without validating its performance is risky and can result in deploying a suboptimal or incorrect model.