Cloud Computing vs. Edge Computing
November 1, 2022
For some time now cloud computing has progressively been integrating itself as a key component in businesses of all sizes. Though cloud computing brings several benefits like the ability to easily scale applications up or down based on actual demand, data traveling back and forth from the source to the cloud adds latency which can make it infeasible for certain use cases like autonomous vehicles. As more enterprises embrace the benefits of AI, this has led to exploring how well their MLOps can extend to edge environments, which are not centralized by their nature, to deploy, test, and observe models on an ongoing basis.
Why Enterprises Have Their Head in the Clouds
At a glance, cloud computing is a description of the technology that provides virtual IT infrastructures including data storage, servers, software, and networking. It can consolidate and divide computing power, regardless of the limitations posed by physical hardware. Cloud computing allows users to optimize the usage of their computational infrastructure as one physical server can be divided into multiple virtual servers dedicated to various uses. Some of its other often highlighted advantages are:
- The capability for using software from a browser or app on any device.
- Cost efficiency by only paying for the computing resources that are needed.
- Easy maintenance for software, hardware, and service updates.
- Ability to quickly scale an application up or down based on actual demand
While there are a variety of MLOps solutions available from various vendors utilizing the advantages of the cloud, challenges still exist for moving models from Proof Of Concept (POC) to production, for example:
- In the healthcare industry, there are concerns with cloud computing regarding security, infrastructure costs, data backup, and sharing of real-time information.
- In manufacturing, detecting model drift is a more complex challenge for cloud computing, as the latency experience of reaching cloud servers in a remote location slows the reaction to trends in this quick-changing industry.
Being Closer to the Edge is Closer to the Solution
In light of these challenges, edge computing moves the ML computations as close as physically possible to the site of its application device or component. This can make the speeds at which you process and store your data far more instantaneous than with cloud computing. Some of the other benefits which serve as solutions for the shortcomings of cloud computing in MLOps are:
- By providing data processing with lower network latency edge computing helps to counter model drift by getting insights with reduced delays.
- Model Deployment at the edge can improve the accuracy of AI/ML models, real-time data sharing, and running multiple models simultaneously.
- Processing data without requiring internet access makes it possible to compute in remote and inaccessible cloud computing locations.
- Processing data at the edge can also save on cloud data transport, storage, and computing costs
With an ML deployment solution like Wallaroo, you can deploy your ML models both via a cloud connection or at the edge. Even in environments with no connectivity to the open internet, Wallaroo’s platform can be loaded on a USB stick or similar portable hard drive allowing a technician to set up the Wallaroo Edge node wherever you need it to be. This allows your edge devices to take in sensor data, run ML inferences more efficiently, and then feed the results back to your application without the concerns found in typical cloud computing scenarios.
Instead of wondering how your MLOps can benefit from running on the edge, reach out to us at deployML@wallaroo.ai to speak with one of our experts and schedule a demo. See how we can improve MLOps by bringing them closer to the edge.
