Databricks Certified Machine Learning - Associate
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You are working on a machine learning project that requires distributed hyperparameter tuning using Spark MLlib and Hyperopt. Your dataset is very large, and you want to ensure that the optimization process is efficient and scalable. What strategies can you employ to achieve this?
A
Use a single-node setup and increase the number of trials to ensure thorough exploration of the hyperparameter space.
B
Parallelize the trials using Spark's distributed computing capabilities and limit the number of concurrent trials to avoid overwhelming the system.
C
Increase the size of the dataset to improve the accuracy of the model, even if it leads to increased computational cost.
D
Disable parallelization and use a simple grid search approach for hyperparameter tuning to reduce complexity.