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Answer: Orchestration and scheduling, which manages the workflow and triggers each phase of the pipeline at the correct time.
The Orchestration and scheduling phase is critical for the automation of the ML pipeline, as it coordinates the execution of all other phases. It ensures that each component of the pipeline, from data collection to model deployment, is triggered at the appropriate time and in the correct sequence. This phase leverages tools and platforms designed to automate workflows, manage dependencies between tasks, and handle triggers based on time or events, making it indispensable for scalable and reliable pipeline operations.
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You are designing an automated Machine Learning (ML) pipeline for a financial services company that processes large volumes of transactional data daily. The pipeline must efficiently handle data ingestion, preprocessing, model training, evaluation, and deployment, with a strong emphasis on reliability and scalability. Given the complexity of the pipeline, which phase is primarily responsible for the scheduling and triggering of pipeline executions to ensure seamless operation across all stages? Choose the best option.
A
Data collection, as it initiates the pipeline by gathering the necessary data for processing.
B
Data preprocessing, where data is cleaned and transformed before model training.
C
Model deployment, which involves making the trained model available for predictions.
D
Orchestration and scheduling, which manages the workflow and triggers each phase of the pipeline at the correct time.
E
None of the above, as pipeline scheduling is handled by an external system not part of the ML pipeline.
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