Google Associate Cloud Engineer Question 144
Single answerGoogle Cloud PlatformYou are tasked with deploying a machine learning model as a data product on Google Cloud Platform. You need to ensure that your deployment is scalable and can handle requests from multiple clients. Which service should you use to deploy your machine learning model?
- A
Google Kubernetes Engine (GKE)
- B
Cloud Functions
- C
Cloud Run
- D
AI Platform Prediction
Show answer and explanation
Correct answer: D
Explanation
AI Platform Prediction is the most suitable choice for deploying machine learning models on Google Cloud. It offers built-in support for model serving, scalability, and load balancing, making it an ideal choice for handling requests from multiple clients efficiently. Other options like GKE, Cloud Functions, and Cloud Run can be used for deployment, but they require additional management and configuration that AI Platform Prediction provides out-of-the-box for machine learning tasks.
- A. Incorrect.
Google Kubernetes Engine (GKE) can be used to deploy scalable containerized applications, but it requires more management and is typically used for more complex applications beyond just deploying a model.
- B. Incorrect.
Cloud Functions is a serverless compute service that is great for lightweight, event-driven applications, but it isn't ideal for deploying complex machine learning models that require scalability.
- C. Incorrect.
Cloud Run is a good option for deploying containerized applications with the benefit of serverless management, but it may require additional configuration for machine learning model deployment compared to AI Platform Prediction.
- D. Correct.
AI Platform Prediction is specifically designed for deploying machine learning models on Google Cloud, providing built-in scaling and optimized performance for handling client requests.