Edge AI – How AI is sparking the adoption of edge computing

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AI applications will require low-latency, local compute for rapid inferencing and large scale data collection, triage, and engineering. Edge compute will therefore play a key role in AI app delivery. However it’s not just about infrastructure – commercial scale for edge AI will depend on effective ecosystem collaboration models.


Format: PDF filePages: 23 pagesAuthor: Tim OttoPublication Date: October 2023

Table of Contents

  • Executive Summary
    • The role of edge AI in driving scale for AI-driven applications
    • Effective ecosystem management will underpin the success of AI at the edge
  • Introduction
    • Enterprises across all industries are investigating how they can leverage AI applications
    • Enterprises will access centralised AI models, developed and trained in the cloud
  • Interest in AI is driving demand for edge but obstacles remain
    • Technical challenges
    • Commercial challenges
    • Enterprises are concerned about the security of AI and protecting their proprietary data
  • An ecosystem approach to edge AI
    • What is an ecosystem?
    • Why are ecosystem approaches necessary for scaling edge AI?
  • Conclusion

Table of Figures

  • Figure 1: Cloud and edge will work in tandem to form AI infrastructure
  • Figure 2: Significant technical and commercial challenges must be addressed before the edge AI ecosystem can achieve meaningful scale
  • Figure 3: Trends in training of machine learning and AI models
  • Figure 4: Horizontal adoption of AI models has exploded in the past 18 months
  • Figure 5: Edge provides low latency, greater reliability, and cost efficiency for AI applications
  • Figure 6: AI will leverage an edge-cloud infrastructure for end-to-end training and inference
  • Figure 7: Technical and commercial challenges to scale AI at the edge
  • Figure 8: Ecosystem business models unlock value for all stakeholders involved


Technologies and industry terms referenced include: AI, AI Ops, Amazon, Aotu, AWS, B2B2x, Bloomberg, ChatGPT, Cloud, device edge, ecosystem business models, edge, edge computing, edge infrastructure, edge orchestration, Intel, IoT, Ipsotek, ISV, LaMDA, LLMs, network edge, Neural networks, on-premise edge, private cloud, Red Hat