Edge Computing and Machine Learning, a strategic alliance
Nowadays, it is not unusual to talk about terms such as Artificial Intelligence or Machine Learning. Society, companies and governments are increasingly aware of techniques such as deep learning, semi-supervised learning, reinforcement learning or transfer learning, among others. However, they have not yet assimilated the many benefits of combining these techniques with other emerging technologies such as the Internet of Things (IoT), Quantum computing or Blockchain
IoT and Machine Learning are two of the most exciting disciplines in technology today, and they are having a profound impact on both businesses and individuals. There are already millions of small devices embedded in factories, cities, vehicles, phones and in our homes collecting the information needed to make smart decisions in areas such as industrial process optimisation, predictive maintenance in offices, people’s mobility, energy management at home, and people’s facial recognition, among others.
The approach of most of these applications is to detect information from the environment and transmit it to powerful remote servers via the Internet, where the intelligence and decision making resides. However, applications such as autonomous vehicles are very critical and require accurate real-time responses. These new performance requirements play a key role in decision-making, and the use of remote servers outside the autonomous vehicle is not appropriate. The main reasons concern the time taken to transfer data to external servers and the permanent need for internet connectivity to process the information
A new computing paradigm is emerging to help alleviate some of the problems above. This approach brings data processing and storage closer to the devices that generate it, eliminating reliance on servers in the cloud or in data centres located thousands of miles away. Edge Computing is transforming the way in which data is processed, improving response times and solving connectivity, scalability and security problems inherent to remote servers.
The proliferation of IoT devices, the rise of Edge Computing and the advantages of cloud services are enabling the emergence of hybrid computing where the strengths of Edge and Cloud are maximised. This hybrid approach allows tasks to be performed in the optimal place to achieve the objective, whether on local devices, on cloud servers, or both. Depending on where the execution takes place, the hybrid architecture coordinates tasks between edge devices, edge servers and cloud servers:
Research in the field of Machine Learning has made it possible to develop novel algorithms in the context of the Internet of Things. Although the execution of these algorithms is associated with powerful cloud servers due to the computational requirements, the future of this discipline is linked to the use of analytical models within edge devices.
