Offline batch inference is a critical workload for many AI products, and addressing its challenges requires a solution that can manage compute infrastructure, optimize resource utilization, transfer data efficiently, and provide a user-friendly experience. Ray Data emerges as the best practical solution for offline batch inference, outperforming AWS SageMaker Batch Transform and Apache Spark by up to 17x and 2x respectively in image classification benchmarks. Its ability to scale effectively to terabyte-sized datasets, stream data through CPU and GPU stages, and support heterogeneous clusters makes it an ideal choice for deep learning workloads. Ray Data's Python native programming model, native support for multi-dimensional tensors, and autoscaling capabilities further enhance its performance and user experience.