Cloud Native Edge - Critical for leveraging AI/ML and 5G

While Artificial Intelligence (AI), Machine Learning (ML), IoT, Edge and 5G are becoming mainstream, we are beginning to understand the inherent synergies and dependencies between these technologies as one coherent system to deliver business value with sustainable ROI.

We have clearly understood the individual value proposition of all these technologies. Several compelling use cases have already been deployed by many customers leveraging AI/ML, 5G and IoT individually. However, we are quickly emerging into a new era in which the competitiveness of these initiatives will largely be determined by how customers will leverage the inter-system synergies of these technologies.

Building "cloud native edge" will provide the critical foundational platform for yielding maximum ROI. We know well that “cloud native” is more about the “operating model” for application development and deployment as much as it is about the enabling technologies like containers, micro services and dynamic orchestration tools.

Embracing cloud native principles for edge will solve one of the most complex “last mile delivery issues” of AI/ML. The term “last-mile problem” comes from the telecom industry, which observed that it costs inordinately more to build and manage the last-mile of infrastructure to the home than to bring infrastructure to the hub city or residential perimeter.

Companies are starting to discover a similar last-mile delivery problem in AI / ML. It is much harder to weave AI/ML technologies into business processes that actually run companies than it is to build the AI/ML models that promise to improve those processes.

Quite simply, it is comparatively easier to collect data through IOT and build and train AI/ML models in a central location like a data center or cloud. However, the complexity is exponentially higher to deploy those models at edge against live data without any disruptions.

This is where embracing cloud native edge will become crucial for the success for such initiatives. I intentionally stayed only on technical challenge related to AI/ML deployment at edge, as there are wider organizational ownership issues required to be solved by companies beyond technical challenges.

In conclusion, “cloud native edge connected with 5G enabling AI/ML” will be the potent force for innovation in this decade.

• This will enable wider adaption of AI/ML and in the long term, the development and deployment of such models will become more automated.
• Needless to state that the IOT devices and the data generated by those devices will continue to grow. Effective management of devices and data, efficient harnessing of data, agile development and deployment workloads (AI/ML models), simplifying management of workloads and the infrastructure which supports those workloads "at scale" and security across multiple environments will all present new challenges.

Inherent synergies of IoT, Edge, AI/ML and 5G as one coherent system built on cloud native principles will be key to solving those challenges. 

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