Bringing AI at the Edge | The Ultimate Guide

Overview of Bringing AI at the Edge

Data analytics, machine learning, deep learning, Artificial Intelligence are technologies that act as fuel for innovation in organizations. Now, edge computing, AI and IoT are not buzzwords anymore. They provide insights that uncover opportunities to deliver new services or to optimize costs. Artificial intelligence has been deployed in the Cloud because AI algorithms require massive amounts of data and consume enormous computing resources. 

Cloud Computing became a necessary component of AI evolution. As customers spend more time on smart devices, stakeholders realize the need to bring basic computation onto the machine only instead of a cloud to serve more customers. Also, in many situations, such as in autonomous vehicles and fraud detection, there is a need to take AI-based data computations and decisions locally, i.e. on devices that are close to the edge of the network to take immediate action. 

Click here to Implement AI in Edge Computing for Automation in Industries

In this Covid-19 era, The pandemic becomes very stressful for the hospitals and Health care providers, and especially when the number of patients sometimes increases exponentially. To deal with such types of crises, a solution is required that allows one clinician to monitor several patients virtually at a time. Various devices such as sensors attached to continuously monitor the COVID patients, along with relevant tests that are generating tremendous amounts of data. Here, we can use the latest technologies that collect enormous data to analyze, mine and discover AI models. These models can then be deployed to Cloud for application areas such as predictive maintenance. But in some situations, if the health of the patient suddenly deteriorates and needs to be informed immediately to the doctor, AI models must be positioned at the edge, where decisions can be made quickly without relying on network connectivity and without migrating massive amounts of data back and forth across a network. This is why the Edge Computing market will continue to accelerate in the following years. At the edge of AI provides mission-critical and time-sensitive decisions to be made quicker, more reliably, and with more prominent security. 

What is Edge AI?

Edge AI is the combination of Edge computing and Artificial Intelligence. With Edge AI, AI algorithms are executed locally on a hardware device using the data collected from Edge computing.

As the data is collected and processed in real-time, it reduces power consumption as well as data costs since the device doesn't need to be connected to the internet at all times. Edge computing brings processing, computation, and data storage closer to where it is generated and collected instead of relying on moving it to a remote location such as a cloud. 

Click to explore the Edge Computing Architecture

Does Edge AI exist?

Yes, it does. There are some accessible real-time instances in which the involved algorithms are used to process the data right in your device instead of sending it to cloud for obtaining results-

  • iPhone registering and recognizing your face for unlocking the phone in milliseconds.
  • Google Maps are pushing alarms about bad traffic.
  • Autonomous vehicles to put emergency brakes if AI algorithms predict any collision.
  • A security camera must recognize intruders and react immediately.
  • If a sensor predicts an explosion in a chemical plant, the plant needs to be shut down immediately.

Why should we care about the edge?

As businesses begin to invest in edge computing as part of their broader technology strategy, it begs the question — why edge? And what makes it so distinct from standard cloud computing? Organizations need to process an ever-growing amount of data, then turn this data into insights and actions faster than ever before to enhance the overall application/user experience for latency-sensitive applications. 

Explore here the Role of Edge AI in Automotive Industry

Drivers of Edge Computing and Edge AI

Edge computing is a distributed computing model that does necessary computations and stores data closer to the location of the device. There is a misunderstanding that edge computing will replace Cloud computing. On the converse, it functions in association with the Cloud. Big data will always be processed on the Cloud. But, instantaneous data produced by the users and associates only to the users can be computed and processed on edge. There are numerous drivers of Edge Computing and Edge AI.

  • Latency - The apparent reason for tasks to be done on edge is latency. The delay while moving data to the Cloud for processing and then results are transmitting back over the network to a local device. In some situations, AI models must be processed at the edge or at the device itself so that decisions can be made faster without relying on network connectivity and moving extensive data back and forth over a network.
  • Privacy - In some scenarios, the sharing of personal and sensitive data (e.g. Finance sector) across boundaries has raised concerns regarding data privacy. Here, AI on edge helps by only sharing the data that requires further evaluation, which decreases the amount of data transferred and reduces the probability of breach in privacy.

Explore the Challenges and Solutions for AI Adoption

  • Performance - AI models can process the data much quicker on the device itself as compared to the Cloud as the data does not need to travel back and forth. But there are still events where data processing at the Cloud is better. When judgments require extensive computational power and do not need to be executed in real-time, AI should stay in the Cloud.  For example, In healthcare, when AI is used to interpret an ECG or to analyze crop quality (in agriculture), data collected by a drone over a farm where one can wait a few minutes or a few hours for the decision, it is better to do this processing in the Cloud. 
  • Bandwidth - To generate insights from AI, data needs to move to the Cloud. As connection speed differs in various parts of the world and sometimes it is not easy to transfer data from/to the server from such remote locations. On the other hand, AI at the edge solves the problem by sending only the part of the required data for further analysis.

A Holistic Approach

AI at the Edge can be used where there is a strict need to extract the insights of the data at the edge and resolve latency issues associated with cloud computing are eliminated.