PDE Preparing and Using Data for Analysis Practice Question
A machine learning engineer needs to deploy a custom TensorFlow model for online predictions with low latency. The model is already trained and saved in SavedModel format. Which Vertex AI service should they use?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Vertex AI Prediction
Vertex AI Prediction allows you to deploy custom models (including TensorFlow SavedModel) to an endpoint for online predictions. It supports autoscaling and low-latency serving.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Vertex AI Workbench
Why it's wrong here
Workbench provides managed notebook environments for developing and training models, not serving them. It tempts because the model was built in a notebook, but online low-latency prediction needs a deployed Endpoint, which Workbench does not supply.
- ✓
Vertex AI Prediction
Why this is correct
Vertex AI Prediction deploys the SavedModel to an endpoint serving online predictions with low latency, satisfying the stem's requirement. Unlike batch prediction, it provisions a persistent endpoint for real-time inference, and unlike custom training, it consumes the already-trained artefact directly without retraining.
- ✗
Vertex AI Feature Store
Why it's wrong here
Feature Store centralises feature storage and serving for training and prediction, but it does not host models. It tempts because online prediction consumes features, yet the SavedModel itself must be deployed to a Vertex AI Endpoint, which Feature Store cannot provide.
- ✗
Vertex AI AutoML
Why it's wrong here
AutoML trains and tunes models from your data, so it cannot serve an already-trained SavedModel. It tempts because it also offers prediction endpoints, but those host AutoML-generated models; deploying custom TensorFlow artefacts requires Vertex AI Endpoints instead.
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