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Have you heard of cloud computing before? You should. When Amazon Web Services (AWS) was introduced a decade ago, it has contributed a lot in the IoT Industry with the notion of cloud computing.

But it took some time for companies to see how it could transform their services and leverage it wisely. They practically had to figure out how the benefits of cloud computing would outweigh the risks and to that effect, decide whether to deploy “on-premise or in the cloud (or is it possible to use both)?”

In the recent years, machine learning came into the picture and the same confusion has enterprises scratching their heads once again. Would machine learning and it’s newest byproduct called deep learning, be able to make a huge difference like AWS did?

There’s a bit of a learning curve to plow through but as predicted, it is again transformative with Gartner’s Hype Cycle for Emerging Technology now saying we are almost reaching that peak point of true productivity in the market. Both machine learning and edge computing have deep impact on how people and the machines we make transform how we work. However, the right expectations must first be set.


The opportunity is here with both knowledge and resources coming together at the best time. That means to say, the time is ripe for companies to invest in learning, leveraging and integrating into machine learning and edge computing.

Now is the best time while expectations for these technology to broaden the potential of parallel industries to grow with IoT is HIGH. That means to say, while opportunities are ripe, we must invest in it before you’re the only one left out.

Developers know this is the right time to reach out to companies as well. There is still a large untapped market that have heard of machine learning and intelligent edge but unsure what the first steps are. Some literally still cannot make sense of it all.

However, there are companies that have acquiescence into the technology of intelligent edge but may not know exactly what they are doing. With the right guidance, you will be guided right way of choosing which algorithm works best and where to deploy them.