The correct choice is Vertex AI Platform because it provides a unified managed environment for data preparation, training, hyperparameter tuning, deployment, monitoring, and governance for both custom models and generative AI, and it lets your team manage many models in one place.

The platform supports repeatable workflows with pipelines and built in tuning that scales to many experiments. It includes a model registry for lineage and versioning and it offers endpoints for online and batch prediction with evaluation and drift monitoring. It also integrates foundation model capabilities for generative AI while keeping your projects, artifacts, and monitoring centralized, which aligns with the need to govern dozens of models quickly.

BigQuery is a serverless data warehouse and analytics service and while BigQuery ML can train models, it does not provide a full lifecycle platform with experiment tracking, a model registry, deployment endpoints, and production monitoring in one place.

Google AI Studio focuses on prototyping and prompt design with Gemini and on API key management, and it is not designed to orchestrate data preparation, training, tuning, and deployment pipelines across many governed models.

Google Kubernetes Engine manages containers and clusters and it can host ML workloads, but it lacks the managed ML features such as pipelines, experiment tracking, hyperparameter tuning, a model registry, and monitoring that you expect in an end to end ML platform.

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