You have a set of machine learning models that you want to deploy and manage at scale. These models were not trained on Google Cloud and are in different formats. What GCP service should you consider using to meet these requirements?

*Explanation:* To deploy and manage machine learning models at scale, particularly when they are in different formats and not trained on Google Cloud, the most suitable option is to use AI Platform Prediction with custom runtime support. This allows for the deployment and scaling of models in custom formats, making it a versatile and scalable choice. Other options such as Compute Engine with custom containers or App Engine with manual scaling may not offer the same level of flexibility and automation. Converting all models to TensorFlow might be impractical and time-consuming, while utilizing Kubernetes Engine with Kubeflow could introduce unnecessary complexity for model deployment.