Edge computing is defined as solutions that move data processing at or near the point of data generation. This means that the results of Machine Learning model inference can be delivered to customers faster and create a real-time inference feeling. This is a perfect place for your models. Looking at Gartner's prediction: "Around 10% of enterprise-generated data is created and processed outside a traditional centralized data centre or cloud. By 2025, Gartner predicts this figure will reach 75%".

We have a vision of a Network Compute Fabric where the lines between networking and computing disappear. On the journey there, edge cloud computing provides a critical stepping-stone where computing is pushed very close to where it is needed. This distribution of computing capabilities in the network creates new challenges for its management and operation. We argue that a data-centric approach that extensively uses artificial intelligence (AI) and machine learning (ML) technologies to realize specific management functions is a good candidate to tackle these challenges. As can be seen in Figure 1, edge computing services can be provided through compute/storage resources at different locations in a network, such as on-premises at a customer/enterprise site (industrial control, for example) or at access and local/regional sites (telco operators, for example).

As the adoption of artificial intelligence (AI), deep learning, and big data analytics continues to grow, it is becoming increasingly important for edge computing systems to process large data sets in a timely and efficient manner. The basic compute, storage and networking capabilities are all present today at the edge, but speeds and capacity will only continue to increase and advancements like NVMe (Non Volatile Memory Express) will offer significant performance advantages and boost AI adoption at the edge. It is possible, and becoming easier, to run AI and machine learning with analytics at the edge today, depending on the size and scale of the edge site and the particular system being used. While edge site computing systems are much smaller than those found in central data centers, they have matured, and now successfully run many workloads due to an immense growth in the processing power of today's x86 commodity servers. It's quite amazing how many workloads can now run successfully at the edge.

As the adoption of artificial intelligence (AI), deep learning, and big data analytics continues to grow, it is becoming increasingly important for edge computing systems to process large data sets in a timely and efficient manner. The basic compute, storage and networking capabilities are all present today at the edge, but speeds and capacity will only continue to increase and advancements like NVMe (Non Volatile Memory Express) will offer significant performance advantages and boost AI adoption at the edge. Edge-based AI: Are We There Yet? It is possible, and becoming easier, to run AI and machine learning with analytics at the edge today, depending on the size and scale of the edge site and the particular system being used. While edge site computing systems are much smaller than those found in central data centers, they have matured, and now successfully run many workloads due to an immense growth in the processing power of today's x86 commodity servers.