3 years ago

Category: Business, Data, Privacy, artificial-intelligence

Running applications at the edge offers many advantages if architected well, including lower latency and cost effectiveness, as well as easier compliance with privacy and data regulations, especially for data-heavy workloads. The edge exposes the need for a completely new development framework, replete with newer data management APIs and services, novel ways of invoking, slicing and stitching together artificial intelligence/ machine learning toolchains, and a new set of energy-efficient AI/ML algorithms for computer vision and natural language understanding (NLU).

Currently, many of the major cloud service providers (CSPs) are trying to tailor and extend their cloud platform – for example, Google Anthos or Amazon Outpost – to solve the edge development and operational challenges. The rationale being quite simple: to bring to the edge the same development and operational paradigm a customer is used to in their public cloud and the promise of seamlessness, especially if you have bet on a singular cloud provider.

To enable a new slew of edge applications, developers need help with two categories of edge services: one around new APIs for application development, and another for simplified life cycle management.