Google Professional Machine Learning Engineer Question 361
Select 3Google Cloud PlatformYou are tasked with developing an end-to-end machine learning pipeline on Google Cloud for a fraud detection system. The pipeline needs to handle data ingestion, data preprocessing, model training, validation, and deployment. Which of the following are key considerations to ensure an effective and scalable pipeline?
- A
Automating the pipeline to streamline recurring tasks and ensure consistency.
- B
Using a single VM instance for all pipeline stages to reduce costs.
- C
Designing the pipeline to handle data drift and retrain the model as needed.
- D
Implementing monitoring for model performance after deployment.
- E
Manually triggering model retraining workflows to maintain full control.
Show answer and explanation
Correct answers: A, C, D
Explanation
To build an effective and scalable end-to-end ML pipeline on Google Cloud, you need to focus on automation, scalability, and monitoring. Automation helps streamline repetitive tasks, while designing for data drift and implementing monitoring ensure the model remains accurate and reliable over time. Avoiding manual interventions and bottlenecks, like relying on a single VM or manual retraining, is essential for building a robust solution.
- A. Correct.
Automating the pipeline is critical for efficiency and scalability, especially for recurring tasks like preprocessing, training, and deployment. This also reduces the risk of human error.
- B. Incorrect.
Using a single VM instance for all pipeline stages is not scalable or efficient, especially as data size and computational demands increase. It can become a bottleneck in production environments.
- C. Correct.
Designing the pipeline to handle data drift ensures the model remains effective over time by enabling automatic retraining or alerting mechanisms when the input data distribution changes.
- D. Correct.
Implementing monitoring for model performance is essential to detect performance degradation in production and take corrective actions, such as retraining or model updates.
- E. Incorrect.
Manually triggering model retraining workflows is not scalable or efficient for production-grade pipelines. Automated triggers based on performance metrics or schedules are preferred.