The Intersection of Cloud and Edge for an Efficient AI Infrastructure
Cloud and Edge computing are highly sought-after technology with each being preferred depending on the need by the user or business.
cloud computing is the delivery of computing services that include servers, storage, databases, networking, software, analytics, and intelligence over the Internet (ie the cloud)
Edge computing is a distributed computing framework that brings enterprise
applications closer to data sources such as IoT devices or local edge servers.
Although edge computing is preferred over cloud computing in remote locations, where there is limited or no connectivity and where local storage similar to a mini data center is required.
Edge Computing cannot replace or nullify cloud computing because there may likely be a need to continue with centralized processing for some time. Instead, edge computing can be said to be a response to cloud computing, a way of covering for some of its shortcomings.
When it comes to building AI infrastructures, combining cloud and edge services can help engineers achieve a more efficient infrastructure.
Hence a paradigm that creates AI workflows that cut across centralized data centers (the cloud) and devices outside the cloud that are closer to humans and physical things (the edge).
“We can think of the edge as an extension of the cloud”.
Many services utilize both cloud and edge infrastructure. These services are often purpose-built to be an end-to-end solution for AI lifecycle management, removing the complexity of building and maintaining an edge software platform by offering straightforward deployments, and detailed monitoring capabilities with security protocols to protect intellectual property and application insights from cloud to edge.
Many organizations are deploying edge AI to address the limitations of cloud-only platforms like latency and the cost of scaling. However, edge computing has its own set of challenges. For enterprises gathering insights from several multiple locations, installation of hardware, deployment of software, and maintenance at individual locations are expensive and consume time, and the professionals needed to execute these tasks and provide support can be difficult to arrange for several locations.
Cloud infrastructure can help us manage and scale AI deployments across servers and edge devices allowing us to securely and remotely manage deployed systems.
In this scenario, it can be vice versa where cloud computing can be said to be a response to edge computing, a way of covering for some of its shortcomings also.
Then, we can as well think of the cloud as an extension of the edge.
