How to slash 'time to insight' when training AI -- GCN

When agencies seek to develop or improve upon artificial intelligence applications, they often find that many of today's IT systems are not robust enough to manage AI workloads at scale -- nor can they scale up and offer security at the speed required for AI modeling. This is especially true for legacy IT systems that are not purpose-built, AI-capable infrastructures. In fact, many infrastructures used today for AI have been force-fit -- and mis-fit -- into the AI space. Before we look at what scale is required, and what IT infrastructure model is ideal, let's quickly define the stages of advanced AI and machine learning development. In AI development, there is an initial training stage in which an AI practitioner will run AI model after model after model, drawing from deep wells of existing data. Since AI development is iterative, the data used in the training stage is often required to be live, and therefore sensitive.