Home › GCP ML Engineer › cloudcomputing › mleserving › Which Vertex AI capability should an ML Engineer…
Which Vertex AI capability should an ML Engineer use to serve XGBoost / LightGBM / scikit-learn / PyTorch / TensorFlow models from a UNIFIED endpoint API?
ACloud DNS
BBuilding a separate REST API server per framework
CVertex AI Prediction with framework-specific prebuilt containers (or custom container for niche frameworks)
DCloud Memorystore
Answer & Solution
Correct answer: C. Vertex AI Prediction with framework-specific prebuilt containers (or custom container for niche frameworks)
Vertex AI Prediction unifies multi-framework serving (per PMLE §4.1). The other options fragment serving.
Related questions
Which GCP service should an ML Engineer use to serve a model directly inside BigQuery as aWhich GCP-compatible streaming runtime should an ML Engineer use to MAKE PREDICTIONS over Which Vertex AI feature should an ML Engineer use to deploy a CUSTOM container that wraps Which Vertex AI deployment option should an ML Engineer choose for a HIGH-THROUGHPUT onlinWhich ML model-optimisation technique should an ML Engineer use to REDUCE inference latencWhich Vertex AI Feature Store capability should an ML Engineer use to FETCH a feature vectWhich Vertex AI endpoint type should an ML Engineer recommend to keep model traffic INSIDEWhich Vertex AI feature should an ML Engineer use to run an A/B test between two model ver