rts reusing an entire subgraph component. Soon, the community of machine learning experts contributing open source modules will create the potential for deep learning versions of GitHub and StackOverflow.
Hardware. Finding the optimal mix of GPUs, CPUs, cloud resources; determining the level of parallelization; and performing cost analyses are complex decisions for developers. This creates an opportunity for platform and service-based companies to recommend the right infrastructure for training tasks. Additionally, there will be companies that provide infrastructure services—such as orchestration, scale-out, management, and load balancing—on specialized hardware for deep learning. Moreover, I expect incumbents as well as start-ups to launch their own deep learning-optimized chips.
These are just some of the possibilities. I’m certain there are many more lurking in other entrepreneurial minds, because the promise of this technology is immense. We are beginning to build machines that can learn for themselves and that have some semblance of sensible judgment.
Palak Dalal (HBS ’17) contributed research and analysis to this piece.