Faster and Cheaper Offline Batch Inference with Ray

Explore the capabilities of Ray Data, an open-source library for large-scale data processing in machine learning applications, with a focus on offline batch inference. Learn how to efficiently process terabytes of data using pretrained models, understand the limitations of traditional data processing tools for modern deep learning applications, and discover how to leverage Ray Data for faster and more cost-effective inference compared to solutions like Spark or SageMaker. Delve into the importance of offline inference for LLM workloads and witness a demonstration of an end-to-end offline batch inference use case. Gain insights into user success stories and understand why Ray Data is considered the optimal solution for offline batch inference and processing, particularly when working with unstructured data and deep learning models.