Professional Data EngineerOperationalizing machine learning modelsEasy
A data science team has developed a new machine learning model to predict customer churn. The model is currently deployed to a Vertex AI Endpoint for online predictions. To ensure the model continues to perform optimally over time, the team needs to regularly retrain it with fresh data and deploy the updated version without causing downtime for the prediction service. Which Vertex AI feature should they leverage to achieve this seamless update process?
- AVertex AI Experiments
- BVertex AI Feature Store
- CVertex AI Endpoint Traffic Split
- DVertex AI Model Monitoring
Show answer & explanationAnswer & explanation
Correct answer: C. Vertex AI Endpoint Traffic Split
Vertex AI Endpoint Traffic Split allows you to deploy multiple model versions to a single endpoint and route a percentage of incoming traffic to each version. This enables canary rollouts, A/B testing, and seamless model updates without service interruption.
Why the other options are wrong
- A. Experiments are for tracking and comparing different model training runs, not for managing live model deployments.
- B. Feature Store manages and serves features for ML models, it's not directly involved in model deployment strategies.
- D. Model Monitoring is used to detect data drift, concept drift, and model performance degradation, not for seamless deployment.
Vertex AI Endpoint Traffic Split
A Vertex AI feature that allows distributing incoming prediction requests across multiple deployed model versions on a single endpoint.
- Enables canary deployments and A/B testing.
- Ensures zero-downtime model updates.
- Managed at the Vertex AI Endpoint level.
Memory trick: Traffic lights guide the new model smoothly onto the road.