Author: Julie Fewell
The trick to designing an IoT cloud solution for scale is to find the correct balance and optimise the trade-offs between the different options.
The question shouldn’t be ‘is an MVP too time-consuming and costly to engage in?’, but ‘can you afford not to apply consumer insights from MVP deployments to your mass-market product?’
As organisations scale up from limited trials involving thousands of devices, to hundreds of thousands, the total cost of the offering can spiral upwards, out of control.
It’s estimated there’ll be over 30 billion active IoT devices in 2025, up from 12 billion in 2020. This dramatic growth is mirrored in the demand for Edge AI. In our article Edge AI: Making smart choices for smarter devices, we outline a number of advantages that Edge AI brings. But Edge AI chips – powerful and tiny as they may be – are not, on their own, the answer to every Edge problem. For many applications the Edge devices are the workhorses of the processing but are part of a connected network of data from other locations and devices including the cloud.
In the case of AI, big is not always better. This article explores the benefits of Edge AI and how to get started.