Google Professional Machine Learning Engineer
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In the context of automating Machine Learning (ML) pipelines, monitoring and logging play a pivotal role. Considering a scenario where an ML pipeline is deployed in a production environment with high throughput requirements and strict compliance standards, which of the following best explains why monitoring and logging are crucial? Choose the two most correct options.
A
They provide real-time insights into pipeline performance, enabling proactive optimization and efficiency enhancements.
B
They add significant overhead to the pipeline, making it less efficient and more complex without tangible benefits.
C
They facilitate the early detection and resolution of operational issues such as bottlenecks, errors, and performance degradation, ensuring reliability.
D
They replace the need for continuous data preprocessing by automating all necessary adjustments based on logged data.
E
Both A and C are correct because they highlight the dual benefits of performance optimization and operational reliability.