In MLOps, inference refers to using a trained machine learning model to generate predictions from new data, and it can be performed in two main ways: online inference and batch inference. Online inference provides real-time predictions by processing individual requests instantly, making it suitable for applications like recommendation systems, fraud detection, and chatbots where low latency is critical. In contrast, batch inference processes large volumes of data at scheduled intervals, which is more efficient for use cases like report generation, analytics, and offline predictions. Both approaches have different trade-offs in terms of speed, cost, scalability, and system complexity. In your opinion, how should organizations decide between online and batch inference, and what factors are most important when designing inference pipelines in production MLOps systems?