Today, across all fields of endeavor smart organizations are putting their data to work to deliver actionable insights using Artificial Intelligence (AI) to power these results. Despite the investment into AI, organizations often underestimate the impact that AI has on its data management infrastructure and more often than not, they find that they don’t have the needed tools in place to optimize data analysis and realize its potential.
Adding a few sensors to an asthma inhaler opens a huge opportunity to correlate usage and location information among patients. In the United States alone, there are 25 million asthma sufferers—that’s 1 in every 13 people. Combining patient data with information on weather, air quality, pollen counts, and so on in real time can help patients avoid potential triggers, thereby minimizing risk and improving overall health. Cambridge Consultants, a NetApp customer and AI partner, is exploring the potential of smart inhalers using the same NVIDIA and NetApp technologies in ONTAP AI.
The smart inhaler example is a good model for thinking about the requirements of AI at scale. Data flows from thousands of devices at the edge. That data is combined with outside data sets during training in a core on-premises data center with GPU acceleration. The resulting inference model is deployed in the cloud to analyze new data points and identify and act on trigger events.
A bottleneck at any point results in expensive infrastructure, increases costs, and data scientists waste valuable time troubleshooting infrastructure and waiting for results. That alone is bad enough, but in the smart inhaler example and many other use cases, bottlenecks also put outcomes at risk: it does a patient little good to find out about a trigger event after they’ve had an attack.
The proven architecture of ONTAP AI, fueled by a data pipeline from edge, to core, to cloud, delivers an integrated, simple, and high-performance solution to capitalize on the full promise of AI.
A version of this article originally appeared on GovDataDownload.