Edge AI Deployment with Argos |
NEWS |
To achieve the vision of distributed intelligence and real-time automation, enterprises are seeking to deploy Artificial Intelligence (AI) as close to the location of data processing workload as possible. Ideally, this means AI in all edge devices, ranging from large connected vehicles to tiny sensors in electrical and mechanical equipment. However, the realization of such a vision is met with multiple challenges. The diversity of edge devices means any enterprises that want to use custom AI solutions in their devices needs to identify, develop, and trial the right interface, middleware, framework, and cloud solutions that are optimal for edge AI. This requires investing in an AI development team with skillsets in data science, firmware and software technologies, app interface, and full stack development, resulting in heavy investments and long development cycles. This is also the reason why many machine learning projects are struggling to move past the testing and trialing phase, and those that make it through struggle with operationalization, diagnostics, monitoring, and maintenance.
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