Artificial Intelligence (AI) is increasingly being leveraged to elaborate large and growing amount of data, with more accurate and faster results than traditional computer programming.

Inspired by human behaviors and cognitive processes, AI should not be confused with Machine or Deep Learning. Machine Learning is a branch of AI focused on developing computer algorithms and applications that learn from data and automatically improve their accuracy over time through experience. Deep Learning refers to a subset of Machine Learning methods based on artificial neural networks with representation learning.

Machine Learning is particularly useful and interesting in the Internet of Things (IoT) world due to the large number of devices collecting and sharing non-homogeneous data. After going through a statistical analysis process, those data are a treasure trove for the development of new smart applications. In an IoT network we can acknowledge three different layers, each one characterized by the availability of different resources and data where Machine Learning is applicable:  IoT devices, edge devices and the cloud.

At Paradox Engineering, we have integrated the most recent Machine Learning techniques into our IoT platform components, enabling valuable use at different levels, from cloud to edge and IoT devices, making the most of their peculiarities and functionality.

We master consolidated technologies such as TensorFlow, Doors, and Google Colab. Our IoT solutions provide a Machine Learning development framework to allow these technologies to be used in multiple application cases in a simple and transparent way. Developers can easily integrate Machine Learning as enabler for new information-driven functionalities or to enhance existing ones.