The field of machine learning is moving towards more efficient and scalable infrastructure to support the growing demand for large-scale data processing and complex model training. Recent developments focus on optimizing data loading, feature management, and spatial data processing to reduce latency and improve overall system performance. A key trend is the integration of machine learning optimizations into database operations, such as spatial joins, to enhance efficiency and reduce computational overhead. Additionally, there is a growing interest in developing lightweight and easy-to-deploy solutions for vector data management and semantic search capabilities. Noteworthy papers include LIRA, which proposes a learning-based query-aware partition framework for large-scale ANN search, and MLKV, which presents an efficient and extensible data storage framework for large embedding model training. FeatInsight and SOLAR also demonstrate significant advancements in feature management and spatial data processing, respectively.