Optimization tips and tricks on Azure SQL Server for Machine Learning Services


By using memory-optimized tables, resume features are stored in main memory and disk IO could be significantly reduced. If the database engine server detects more than 8 physical cores per NUMA node or socket, it will automatically create soft-NUMA nodes that ideally contain 8 cores. We then further created 4 SQL resource pools and 4 external resource pools [7] to specify the CPU affinity of using the same set of CPUs in each node. We can create resource governance for R services on SQL Server [8] by routing those scoring batches into different workload groups (Figure.

Support Vector Machines and Kernel Methods: The New Generation of Learning Machines

AI Magazine

Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, statistics, and functional analysis to achieve maximal generality, flexibility, and performance. These algorithms are different from earlier techniques used in machine learning in many respects: For example, they are explicitly based on a theoretical model of learning rather than on loose analogies with natural learning systems or other heuristics. Although the research is not concluded, already now kernel methods are considered the state of the art in several machine learning tasks. Their ease of use, theoretical appeal, and remarkable performance have made them the system of choice for many learning problems.