Google Associate Cloud Engineer Question 145
Single answerGoogle Cloud PlatformAs a Google Cloud Associate Cloud Engineer, you are tasked with deploying a machine learning model as a RESTful API for your data science team. Which Google Cloud Platform service should you use to easily deploy and manage your model as a scalable API?
- A
Google Kubernetes Engine
- B
Cloud Functions
- C
AI Platform Prediction
- D
Cloud Run
Show answer and explanation
Correct answer: C
Explanation
AI Platform Prediction is the optimal choice for deploying machine learning models as RESTful APIs because it provides an easy-to-use interface for model deployment, scaling, and management. It is specifically tailored for machine learning tasks and eliminates much of the operational overhead associated with running models in production.
- A. Incorrect.
Google Kubernetes Engine (GKE) can be used to deploy containers, but it requires more setup and management to deploy a model as a RESTful API compared to AI Platform Prediction.
- B. Incorrect.
Cloud Functions is suitable for deploying small functions and event-driven code, but not ideal for deploying ML models as a scalable API.
- C. Correct.
AI Platform Prediction is specifically designed for deploying machine learning models as scalable RESTful APIs with minimal setup, making it the best choice for this task.
- D. Incorrect.
Cloud Run can deploy containers and is suitable for stateless services, but AI Platform Prediction offers more ML-specific features such as versioning and resource optimization.