Google Professional Machine Learning Engineer Question 298
Select 3Google Cloud PlatformYou are deploying a machine learning model to serve predictions in production using Google Cloud. The model needs to handle high traffic, provide low latency, ensure consistent updates without downtime, and integrate well with other Google Cloud services. Which of the following considerations should you prioritize when designing your model serving solution?
- A
Use Google Kubernetes Engine (GKE) with a custom container for serving the model to ensure scalability and flexibility.
- B
Leverage Vertex AI Prediction to serve the model, as it provides managed infrastructure and handles auto-scaling.
- C
Ensure the model server is designed to support batch predictions only, as it is more efficient for high traffic.
- D
Implement canary deployments for model updates to minimize risks and ensure stability during changes.
- E
Choose Cloud Functions for serving the model to benefit from serverless scaling and low operational overhead.
Show answer and explanation
Correct answers: A, B, D
Explanation
When serving machine learning models in production, considerations such as scalability, low latency, managed infrastructure, and safe updates are critical. Google Kubernetes Engine (GKE) and Vertex AI Prediction are both viable solutions that provide scalability and integration with Google Cloud services. Canary deployments ensure that model updates are deployed safely. However, batch predictions and Cloud Functions are not suitable for the real-time, high-traffic use case described in this scenario.
- A. Correct.
Using Google Kubernetes Engine (GKE) with a custom container is a valid approach for serving models in production. GKE provides scalability, flexibility, and control over the deployment, which is essential for handling high traffic and ensuring low latency.
- B. Correct.
Leveraging Vertex AI Prediction is a strong choice for serving models, as it is a managed service that simplifies infrastructure management, provides auto-scaling, and integrates seamlessly with other Google Cloud services.
- C. Incorrect.
Batch predictions are not suitable for use cases requiring low latency and real-time predictions. This consideration does not align with the requirements in the scenario.
- D. Correct.
Canary deployments are a best practice for updating models in production. They allow you to test new versions incrementally, minimizing risks and ensuring stability during updates.
- E. Incorrect.
While Cloud Functions is a serverless option, it is generally not ideal for serving machine learning models that require low latency and high scalability. It is better suited for event-driven or lightweight tasks.