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You are a machine learning engineer at a fintech company that has developed several models for various use cases, including fraud detection, credit scoring, and personalized marketing. Each model has different performance and deployment requirements. The fraud detection model requires real-time predictions with low latency and needs to scale quickly based on incoming transaction volumes. The credit scoring model is computationally intensive but can tolerate batch processing with slightly higher latency. The personalized marketing model needs to be triggered by events and doesn’t require constant availability. Given these varying requirements, which deployment target is the MOST SUITABLE for each model?