The organizations generating the most market value from their products (consumer data) have been those successful at flipping the consumer-producer dynamic on its head. Having successfully marketed a platform for users to provide intimate demographic trends, from social media to search engines, their value generation now is leveraging A.I. heuristics to identify patterns in large volumes of proprietary consumer data. Instead of selling a product, they offer it for free and then ask for something else valuable in return — they offer compelling utility and entertainment, and with COVID-19 making in person visits more difficult, their use has only been reinforced as people have craved company during isolation.
However, not all organizations monetize privacy invasion, and HPC assets demonstrate strong ROI across verticals. Their proliferation can be contextualized by this feedback loop: HPC assets are initially invested in because AI/ML drives novel and unique insight; business value and profitability grow from improved efficiency; excess revenue pays for neural networks to be tweaked, trained, and upgraded for better insights faster; and the process repeats ad infinitum.
Neural net analysis is broadly useful, so much so that even middlingly large organizations want to employ their use immediately, even with little or no relevant technical expertise. This is why cloud providers are so attractive, their automated platforms and compute resource pools make it easier and cheaper, initially, for an organization to take the first step towards AI/ML. Depoloying air gapped HPC to bare metal is not trivial. They are manually configured, hand-built instances which are uniquely configured to suit the needs of each individual organization.
Unfortunately, human error is endemic to manual configuration – complex neural network applications with equally formidable infrastructural substrates could take months of configuration for an internal team or worse, expensive consultants, to implement. For example; what if there are poor, confusing, or missing comments describing configuration changes; what if something breaks and another reconfiguration is needed; what if other inefficiencies or poor configurations are discovered that need to be addressed before the project can be completed; what if the scope of reconfiguration grows outside of projected tolerances; or what if critical team members leave? Justifying the interruption of valuable compute cycles to push new configurations is difficult; organizations must focus on identifying an air gapped infrastructure streamlining configuration and change-management capabilities so security can be made practical.
For companies trying to glean beneficial insights from existing HPC, instead of focusing solely on an arms-race defined by compute power, consider the underlying programmatic infrastructure of leading-edge networks. The powerful automation and system integration of cloud technology is key to providing convenience to customers. On premises enterprise configuration in general is an expensive and delicate task requiring support from IT staff or consultants, and more specifically for HPC and manufacturing, misconfiguration cuts into valuable compute and production cycles. Replacing the bespoke, manual configuration of an air gapped HPC with NetThunder's automated private cloud infrastructure removes the programmatic burdens of change management and deployment. In response to the off premises IT support model, with concerns over the growing frequency and damages caused by threat actors, NetThunder has developed a private cloud module with all the creature comforts of automation.