These sources collectively discuss advancements in scalable, efficient, and secure machine learning (ML) data systems, often within the context of large-scale datacenter deployments. Several papers address the performance and security trade-offs of using Confidential Computing (CC) and Trusted Execution Environments (TEEs) for large language models (LLMs) and database systems, including utilizing technologies like Intel TDX and specialized frameworks for FPGAs. Other documents focus on optimizing the ML training data pipeline, detailing systems like RecD for deduplication in deep learning recommendation models (DLRMs) to improve efficiency and cedar, a framework for automated pipeline optimization that addresses bottlenecks in data preprocessing, caching, and operator reordering. Finally, one source introduces MinionS, a collaboration protocol between small on-device LMs and frontier cloud LMs designed to significantly reduce remote inference costs while maintaining high performance for data-intensive reasoning tasks.Sources:https://arxiv.org/pdf/2505.16501https://arxiv.org/pdf/2502.15964https://hazyresearch.stanford.edu/blog/2025-05-12-securityhttps://arxiv.org/html/2411.03357v1https://purl.stanford.edu/dm268wp3942https://stacks.stanford.edu/file/dm268wp3942/mark_zhao_dissertation-augmented.pdfhttps://arxiv.org/pdf/2502.11347