Databricks Certified Machine Learning - Associate
Get started today
Ultimate access to all questions.
Ultimate access to all questions.
Consider a scenario where you are tasked with scaling a machine learning model to handle a dataset that is too large to fit into a single machine's memory. Describe the challenges you might face in distributing this model across multiple nodes and how Spark ML can help address these challenges.
Explanation:
Distributing a machine learning model across multiple nodes involves challenges such as data partitioning to ensure balanced workload distribution, and synchronization to maintain consistency across nodes. Spark ML provides mechanisms like resilient distributed datasets (RDDs) and DataFrame APIs that facilitate efficient data partitioning and synchronization, thereby helping to manage these challenges.