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Answer: A pipeline in Spark ML includes multiple stages like data preprocessing, feature extraction, model training, and evaluation; stages are executed in sequence or in parallel based on the pipeline design.
A machine learning pipeline in Spark ML is a sequence of stages that process data and build a model. These stages can include data preprocessing, feature extraction, model training, and model evaluation. Each stage processes the data and passes it to the next stage. The pipeline ensures that all stages are executed in the correct order, and the data flows seamlessly through each stage, facilitating a structured and efficient machine learning workflow.
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Describe the process of building a machine learning pipeline in Spark ML. What are the key components of a pipeline, and how do they interact with each other during the training and evaluation phases?
A
A pipeline in Spark ML consists of data ingestion and model training stages; they interact sequentially.
B
A pipeline in Spark ML includes multiple stages like data preprocessing, feature extraction, model training, and evaluation; stages are executed in sequence or in parallel based on the pipeline design.
C
A pipeline in Spark ML is a single-stage process focusing only on model training; no interaction between stages.
D
A pipeline in Spark ML involves outsourcing stages to external services; interactions are managed via APIs.
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