Databricks Machine Learning Associate Question 580
Single answerYou are training a machine learning model on a Databricks cluster and need to perform hyperparameter tuning using a single-node scikit-learn model. You also want to parallelize the hyperparameter search across multiple worker nodes in the cluster. Which approach would allow you to achieve this?
- A
Use the scikit-learn
GridSearchCVdirectly within a Databricks notebook. - B
Leverage the
jobliblibrary with scikit-learn to distribute the workload across the cluster. - C
Use the
spark_sklearn.Converterutility to parallelize scikit-learn operations on Spark. - D
Implement hyperparameter tuning using Databricks MLflow with distributed SparkTrials.
Show answer and explanation
Correct answer: D
Explanation
While scikit-learn and related tools like GridSearchCV or joblib provide parallelism capabilities, they are limited to single-node processing. To fully distribute the hyperparameter tuning process across a Databricks cluster, MLflow's SparkTrials is the recommended approach. SparkTrials integrates with Spark to parallelize the search process, enabling efficient use of cluster resources.
- A. Incorrect.
Using
GridSearchCVdirectly in a Databricks notebook will not distribute the workload across the cluster; it will run on a single node. - B. Incorrect.
The
jobliblibrary provides parallelism within a single machine, but it cannot distribute workloads across multiple nodes in a Databricks cluster. - C. Incorrect.
The
spark_sklearn.Converterutility enables Spark integration with scikit-learn for distributed predictions and pipelines, but it does not support hyperparameter tuning. - D. Correct.
Using MLflow with SparkTrials is the correct approach, as SparkTrials enables distributed hyperparameter tuning across a Databricks cluster by leveraging Spark's parallelism.