Google Professional Machine Learning Engineer Question 193
Single answerGoogle Cloud PlatformYou are leading a machine learning team tasked with building and deploying a recommendation system. The team needs an environment for experimentation, model training, and production deployment. The team is considering Google Cloud services for this workflow. Which of the following Google Cloud environments should you choose to best support this end-to-end machine learning lifecycle?
- A
AI Platform (Vertex AI)
- B
Cloud Functions
- C
Cloud Run
- D
BigQuery
Show answer and explanation
Correct answer: A
Explanation
Vertex AI is specifically designed to support the full machine learning lifecycle on Google Cloud. It allows teams to perform tasks such as data preprocessing, model training, hyperparameter tuning, deploying models to production, and monitoring model performance in a single integrated platform. Other services like Cloud Functions, Cloud Run, and BigQuery serve specific purposes but do not provide the comprehensive ML lifecycle support that Vertex AI does.
- A. Correct.
Vertex AI is a comprehensive ML platform on Google Cloud that supports the entire machine learning lifecycle, including data preprocessing, model training, experimentation, deployment, and monitoring. It is the most suitable choice for an end-to-end ML workflow.
- B. Incorrect.
Cloud Functions is a lightweight, serverless compute option suitable for event-driven tasks, but it is not designed for the full machine learning lifecycle.
- C. Incorrect.
Cloud Run is used to deploy and manage containerized applications. While you could deploy models here, it lacks the broader ML lifecycle management features required for experimentation, training, and monitoring.
- D. Incorrect.
BigQuery is a serverless data warehouse optimized for querying and analyzing large datasets. While it can assist with data preprocessing, it is not a complete environment for the entire ML lifecycle.