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Question 43 - Professional Machine Learning Engineer discussion

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You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?

Choose 2 answers

A.
Decrease the number of parallel trials
Answers
A.
Decrease the number of parallel trials
B.
Decrease the range of floating-point values
Answers
B.
Decrease the range of floating-point values
C.
Set the early stopping parameter to TRUE
Answers
C.
Set the early stopping parameter to TRUE
D.
Change the search algorithm from Bayesian search to random search.
Answers
D.
Change the search algorithm from Bayesian search to random search.
E.
Decrease the maximum number of trials during subsequent training phases.
Answers
E.
Decrease the maximum number of trials during subsequent training phases.
Suggested answer: C, E

Explanation:

Hyperparameter tuning is the process of finding the optimal values for the parameters of a machine learning model that affect its performance. AI Platform provides a service for hyperparameter tuning that can run multiple trials in parallel and use different search algorithms to find the best combination of hyperparameters. However, hyperparameter tuning can be time-consuming and costly, especially if the search space is large and the model training is complex. Therefore, it is important to optimize the tuning job to reduce the time and resources required.

One way to speed up the tuning job is to set the early stopping parameter to TRUE. This means that the tuning service will automatically stop trials that are unlikely to perform well based on the intermediate results. This can save time and resources by avoiding unnecessary computations for trials that are not promising. The early stopping parameter can be set in thetrainingInput.hyperparametersfield of the training job request1

Another way to speed up the tuning job is to decrease the maximum number of trials during subsequent training phases. This means that the tuning service will use fewer trials to refine the search space after the initial phase. This can reduce the time required for the tuning job to converge to the optimal solution. The maximum number of trials can be set in thetrainingInput.hyperparameters.maxTrialsfield of the training job request1

The other options are not effective ways to speed up the tuning job. Decreasing the number of parallel trials will reduce the concurrency of the tuning job and increase the overall time required. Decreasing the range of floating-point values will reduce the diversity of the search space and may miss some optimal solutions.Changing the search algorithm from Bayesian search to random search will reduce the efficiency of the tuning job and may require more trials to find the best solution1

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asked 18/09/2024
Nakli Pun
29 questions
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