Google Professional Machine Learning Engineer Question 268
Single answerGoogle Cloud PlatformYou are training a machine learning model on Google Cloud AI Platform and need to optimize its performance by tuning hyperparameters. The dataset is large, and the model training process is computationally expensive. Which approach would you use to efficiently tune the hyperparameters while minimizing compute costs?
- A
Grid Search with all possible hyperparameter combinations
- B
Random Search with a fixed number of trials
- C
Bayesian Optimization using Vertex AI Vizier
- D
Manual tuning by testing different hyperparameter values sequentially
Show answer and explanation
Correct answer: C
Explanation
Bayesian Optimization with Vertex AI Vizier is the most efficient method in this scenario because it intelligently explores the hyperparameter space based on prior trial results, reducing computational costs and improving tuning efficiency. While Grid Search, Random Search, and manual tuning are alternatives, they are either more computationally expensive or less effective for large datasets and complex models.
- A. Incorrect.
Grid Search evaluates all possible hyperparameter combinations, which is computationally expensive and unsuitable for large datasets or expensive training processes.
- B. Incorrect.
Random Search explores hyperparameter space randomly but does not leverage prior knowledge from previous trials, making it less efficient compared to Bayesian Optimization.
- C. Correct.
Bayesian Optimization using Vertex AI Vizier is a highly efficient approach for hyperparameter tuning. It uses prior trial results to intelligently explore the hyperparameter space, making it ideal for computationally expensive tasks.
- D. Incorrect.
Manual tuning is time-consuming and error-prone, especially for models with many hyperparameters. It does not guarantee optimal results, unlike automated methods like Bayesian Optimization.