Exploring the infrastructure needs of AI ZDNet

When you're phasing advanced analytics, machine learning, and artificial intelligence into your infrastructure, traditional configurations aren't necessarily up to the task. Applications related to AI can accumulate a large volume of data based on I/O requirements. You'll need to ensure that these attributes are part of your setup: Microsoft Cloud Services, for example, utilize commodity hardware and scale virtually infinitely to handle AI workloads. By using commodity hardware, Microsoft is able to provide storage services over standard protocols like iSCSI, NFS, SMB, CIFS, etc. and more advanced features. Commodity hardware is a growing trend when designing a system to manage large volumes of data.