Cloud to edge

Cloud is maturing and becoming a commonplace. As recent research has shown, 70% of organizations report more than half of their infrastructure exists in the cloud. And it's less and less about migration to the cloud, but increasing speed in adoption of services in the cloud, such as AI tools and finding an acceptable cost-value balance.  

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When looking at cloud adoption versus value creation, though, it becomes apparent that it has been a technology focus most and foremost, rather than a wider, strategic approach to value creation through cloud computing.

 

As the cost of cloud environments is increasing with ever growing workloads, explosion of underlying data and proliferation of tools, CIOs are put on the spot having to defend a case of proving the value of flexibility, innovation capability and resilience. 

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How would you rate your cloud strategy and maturity in cloud adoption? 

Edge computing is transforming the way we process data in digitally networked ecosystems as it allows for processing power closer to the source of data, rather than sending it to a centralized location (on prem or cloud), enabling low-latency computing, improved security posture across highly distributed assets and lower cost on bandwidth and data.

 

As the world becomes more connected, the amount of data generated continues to grow, and real-time insights are required to make robotics and AI-driven machinery work as smart and agile as possible, edge computing is poised to play an increasingly important role in shaping the future of technology.

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Many customers tend to focus on “What workloads to move to the public cloud and what should stay on-prem”, but ultimately, the better strategy is to build for flexibility and resilience, therefore looking into 'how can I build a consistent operating model to place all of the workloads and data where most effective and efficient?".

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Designing an end-to-end, hybrid, edge to cloud architecture which enables seamless computing in a highly distributed is a must for all leading enterprises today and in the near future. 

With the demand for AI-generated content soaring (AI generated 15 billion images in just 1.5 years - outpacing human photography of over 150 years), especially in 3D World applications, user interactions and response times are critical.

 

Edge-cloud computing is the basis for harnessing the power of artificial intelligence, adeptly managing vast volumes of AI-generated data, empowering user interactions, and facilitating collaborative model training. The computational demands of most GenAI models are significant, often necessitating centralized cloud infrastructure, leading to high latency, environmental concerns etc..; leveraging both cloud and edge servers for more efficient and lower-latency processing will be key going forward. 

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Development and deployment of GenAI models is complex an and high risk in nature. When planning for GenAI services, ensure that a holistic assessment of capabilities and infrastructure is in place and take key aspects such as computation & data offloading, data privacy & security, personalization, latency requirements, quality assurance, compliance, and training needs into consideration.