Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which

Question

Options

• AVertex AI Pipelines and App Engine
• BVertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring
• CCloud Composer, BigQuery ML, and Vertex AI Prediction
• DCloud Composer, Vertex AI Training with custom containers, and App Engine

• A
  7% (3)
• B
  79% (34)
• C
  12% (5)
• D
  2% (1)

Explanation

The requirements map directly to three Vertex AI components: (1) Vertex AI Pipelines for scheduled, containerized model retraining workflows using Docker; (2) Vertex AI Prediction for serving online predictions with built-in autoscaling; (3) Vertex AI Model Monitoring for detecting data drift and performance degradation in production. App Engine (A, D) is a general-purpose web app platform not designed for ML model serving with autoscaling GPU/CPU resources. BigQuery ML (C) does not use custom Docker containers. Cloud Composer (C, D) is a managed Apache Airflow service that could orchestrate retraining but is more complex to set up than Vertex AI Pipelines for this use case. Option B is the most cohesive, purpose-built solution entirely within the Vertex AI ecosystem.

No community discussion yet for this question.