The diversity of edge devices means any enterprises that want to use custom AI solutions in their devices need 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 session will focus on evaluating platform and ecosystem options likely to facilitate edge AI commercialization and how these platforms will be monetized in the future. Starting from understanding the challenges during edge AI deployment, the discussion will go through enabling device onboarding, seamless integration with cloud computing and storage, workload orchestration, innovative user interface, and other features that are popular and high in demand. Ultimately, these advancements allows enterprises to reduce CAPEX investment, minimize development time and accelerate their digitization processes.
|Lian Jye Su