The latest release of Ray 2.8 brings significant enhancements to the ecosystem, focusing on data ingestion and profiling capabilities. The addition of support for reading external data sources like BigQuery and Databricks tables allows for seamless integration with popular data stores, augmenting data ingestion for machine learning training. Ray Data now provides real-time metrics on consumption, including bytes spilled, consumed, allocated, outputted, freed, as well as CPU and GPU usage, enabling better monitoring of operations. Furthermore, the release introduces support for AWS Neuron Core accelerators, providing a range of GPU accelerators to improve heterogeneous training and batch processing performance and efficiency. NVIDIA Nsight System is also natively supported on Ray, allowing for profiling GPU-bound tasks and actors, offering insights into job health and progress. Overall, this release aims to enhance ease of use, performance, and stability while expanding Ray's capabilities with experimental features like AWS Neuron Core accelerators and improved data ingestion functionality.