- There are many AI-savvy chipsets on the market right now, each fine-tuned to support specific AI workloads, development frameworks, or vendor platforms.
- But, what if developers could flexibly combine AI-specific hardware resource pools on the fly, on-premises as well as online?
There’s certainly enough buzz in the industry right now about artificial intelligence (AI). If you look beyond the doomsday predictions of a machine uprising, the prevailing view is that AI is a literal Swiss Army knife of circumstance, able to cut through any and all problems, ready to assemble opportunity out of nothing more than data. It seems that every vendor has one or two machine learning (ML) and deep learning (DL) frameworks lying about. It’s no wonder. There’s TensorFlow, Caffe, Theano, Torch, and many, many more to choose from, most of which open source and are quite accessible to the broader developer community. Continue reading “It’s Time to Orchestrate AI Hardware for Maximum Effect”