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?

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A pipeline in Spark ML consists of data ingestion and model training stages; they interact sequentially.

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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.

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A pipeline in Spark ML is a single-stage process focusing only on model training; no interaction between stages.

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A pipeline in Spark ML involves outsourcing stages to external services; interactions are managed via APIs.

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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?