> does not require network access > benefits minimally from scale-on-demand Neither of these is true when dealing with terabytes of data (or more, if you're w

> does not require network access > benefits minimally from scale-on-demand Neither of these is true when dealing with terabytes of data (or more, if you're working with image/video corpora). Many AI/ML problems have stages that are trivially parallelizable - if you can divide your problem into iterations where a subgraph of nodes communicates internally, then sends/receives updates to other subgraphs, it's very similar to an iterative map-reduce algorithm, perfect for networked cloud systems. And as you're tuning your hyperparameters, you don't know what the performance characteristics are, and you will absolutely want to run experiments in parallel, until you find the right settings that you'll use in production. You'd need to invest in a LOT of redundant bare metal to have that capability. As Netflix puts it in this presentation, the key to effective machine learning is iterating often, and that means having a lot of parallelism to bring to bear. https://www.infoq.com/presentations/machine-learning-netflix-2014 https://www.infoq.com/presentations/machine-learning-netflix...