The text discusses batch processing in data engineering and machine learning, specifically focusing on scaling model training with Ray Core APIs. It explains two approaches to conducting batch training: distributed data loading and centralized data loading. The first approach involves reading each independent task into memory to ensure the desired data fits within memory, while the second approach preloads data partitions into the Ray object store and extracts batches from it. The text also describes an optimized approach using Ray's central object store, which reduces training times by 3-5X compared to the previous approaches. It highlights the benefits of this optimization technique, including lower execution and training times, but notes that it may require more memory and CPU resources. Ultimately, the choice between these approaches depends on the specific use case and size of the dataset.