AI Projects at the Edge: How To Plan for Success

Przemysław Krokosz, January 27, 2025

Collected at: https://www.informationweek.com/machine-learning-ai/ai-projects-at-the-edge-how-to-plan-for-success

Artificial Intelligence continues to gain traction as one of the hottest areas in the technology sector. To meet AI’s requirements for processing power we are seeing a race by US vendors to establish data centers worldwide. Google recently announced a $1 billion investment in cloud infrastructure in Thailand, which was followed almost immediately by Oracle’s promise of $6.5bn in Malaysia. Added to this are many similar ventures in Europe, all under the flag of AI development.  

It’s hardly surprising then that people thinking about AI investment, typically think of a cloud-based project. Yet, we are also seeing significant growth in AI deployments at the edge, and there’s good reason for this.   

The Case for the Edge 

Two of the most compelling reasons are the superiority of speed and security that edge computing can offer. Edge’s freedom from dependence on connectivity provides low latency and makes it possible to create “air gaps” through which cyber criminals cannot penetrate.  

These are both vitally important issues. Speed is of the essence in many applications — in hospitals, industrial sites or transportation, for example. A delay in machine calculations in a critical care unit is literally a matter of life and death. The same applies to an autonomous vehicle detecting an imminent collision. There’s no time for the technology to wait for a cellular connection.  

Meanwhile, cybercrime increasingly poses a major threat throughout the world. The 2024 Cloud Security Report from Check Point software and Cybersecurity Insiders, based on conversations with 800 cloud and cybersecurity professionals, found that 96% of respondents were concerned about their capacity to manage cloud security risks, with 39% describing themselves as “very concerned”.  For sectors such as energy, utilities, and pharmaceuticals, security is a top priority for obvious reasons. 

Another reason for considering the edge deployment for an AI implementation is cost. If you have a user base that is likely to grow substantially, operational expenditure may increase significantly in a cloud model. It may do so even more if the AI solution also requires the regular transfer of large amounts of data, such as video imagery. In these cases, a cloud-based approach may not be financially sustainable in the long term.