Back in 2014, we admired big companies who deployed smart logic and live calculating algorithm into their websites in an instant. We understood how it worked but couldn’t quite grasp how it was done. We invested into the understanding and education of the technology because we saw the potential use cases. In the middle of 2016 we made headway and it turned out we weren’t the only tech firm exploring what is known as today, ‘Machine learning’ (also known as computational statistics). Like wildfire, it became a hot topic of adoption for every tech firm. Last year, machine learning was thought to be technology only available to big companies like Google, Amazon or Apple. Now small organizations like Hubnest have begun building products and services using machine learning. We currently have several examples of internal client portals and live websites utilizing our own mix of Machine Learning technology.

What is machine learning? Machine learning is a system that serve unique results from aggregated data. To predict an outcome, past data is logged and statistics and predictive logic analysis will serve curated information or content.
A simple example would be a user shopping for a blue sofa on a furniture website. A Machine Learning website would recommend other blue sofas, but once a blue sofa is added to a cart, the website will no longer recommend sofas and start recommending popular accent pillows or blankets that go with the blue sofa. This adds another layer of logic, and additional layers can be added almost endlessly — completely automated.

A more advanced example would be the specialized machine learning program framework we developed that can enable any external DevOps team to build their very own logic. These logics can be anything from speech and facial recognition, language translation, data classification to object detection.

We make great efforts to understand the web and its users, and this time we truly believe we are at the forefront. By using our business knowledge to find ways to help our clients grow, we shifted our focus from learning new programming languages to creating logic that will do the work. We believe this is going to be the future. That future is today.