Maximize Operational Efficiency and Improve App Performance with AI at the Edge

Vivek Rastogi | January 19, 2023

Edge AI helps companies maximize operational efficiency, improve application performance, and ensure real-time availability of data and trends for immediate consumption.

 

Vivek Rastogi, Senior Vice President at Encora, presented his unique perspectives and thought leadership on the top 10 technology trends shaping the next generation of technology. He spoke to us about AI at the Edge, a game-changing trend that will help businesses meet the pace and quality demanded of software solutions of today and tomorrow.  

 

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What is AI at the Edge and what are its uses? 

Edge AI (Artificial Intelligence) or AI at the Edge is an amalgamation of Edge Computing and Artificial Intelligence (AI). Edge Computing is a distributed computing paradigm, which refers to having data computation and data storage closer to the actual source of data. Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.  

 Hence AI at the edge means processing data and performing computation on a local device or on any edge-enabled devices that are geographically closer to the data collection source on the same network. The idea is to run AI algorithms on a local device or machine.  

 AI at the Edge allows users to get data in real-time since it does not need other systems or internet connections to connect to others. 

 

What AI at the Edge developments will be helpful for businesses in 2023? 

AI at the Edge is expected to gain more speed in 2023.  

Development of the following AI at the Edge areas will drive its role in business this year: 

  • Edge AI and Metaverse
  • AI at the Edge and security
  • Real-Time Deep Learning with AI at the Edge 
  • Breakthroughs in Video Surveillance & Image Capture 
  • Industrial Automation in the Manufacturing Industry 
  • AI at the Edge in Healthcare  

 

Why did Encora select it as a rising trend in 2023? 

Businesses across the globe have been collecting large amounts of data and performing data analysis and predictive analytics using traditional models with Deep Learning (DL) and Machine Learning (ML). There is a need to perform real time analytics, so that the decision-making process is almost instantaneous, and the outcome can be evaluated immediately. 

 Companies will have to rely on AI at the Edge if they want to have real-time data analytics at their fingertips. This kind of access helps operational efficiency and increased productivity within companies. 

 

What makes AI at the Edge different from traditional enterprise AI applications?  

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. AI at the edge means processing data and performing computation using ML/DL algorithms on a local device or on any edge-enabled devices that are geographically closer to the data collection source on the same network.  

 AI at the Edge allows users to get data analytics and visualizations in real-time on edge devices, hence it provides benefits like low latency, low bandwidth utilization, higher security, etc. 

 

Can you speak about your experience with AI at the Edge? 

AI at the Edge is used by everyone around the world on a day-to-day basis. The best examples of Edge devices are self-driving smart cars, video surveillance cameras, Amazon's Alexa, Apple's Siri and Raspberry Pi. The face recognition system used in smartphones is also another example of edge AI devices. 

 Engineering teams at Encora have built Augmented Reality and Virtual Reality (AR/VR) applications and chat bots using AI the Edge devices; which perform complete data computation and analytics on the edge. 

 

In your experience, where do clients stand on the topic? 

Our clients are very well versed with this technology and have also made substantial investments to reap benefits in the near future. As newer Internet of Things (IoT) devices are manufactured, there is a more compelling need for the adoption of this technology. 

 Clients across domains such as HealthTech, Digital Commerce and Security are looking at different scenarios to adopt this technology. There are use cases where computation is needed in real time, like analyzing patient health records, recommending products to users, and predicting property prices. 

 

How can Encora help clients evolve?  

Encora has deep expertise in software development and research, and as a part of the data engineering and data science practice we have been working on this new trend. We are involved in building several AI/ML models which are fine tuned to run on edge devices with lower configuration.  

 There has been special interest in MEC (Multi-Access Edge Computing) as it enables complex AI models to run n Edge cloud for improved performance and scalability. We have trained engineers who can also help clients with the latest trends in Edge AI. 

 

How will AI at the Edge impact software & digital product engineering? 

Product Engineering refers to the process of innovating, designing, and developing a software product. AI at the Edge is a new trend, which will call for much new software development as new IoT and smart sensors are being built. As businesses look for innovative ways to improve their cost, efficiency, and productivity, the need for AI on the edge will become critical. 

 With AI at the Edge, we need to rewrite algorithms to have the data computation and analytics run on the edge devices or on edge cloud. This needs to be developed during the whole digital product engineering phase. Also, fine tuning these models will add another dimension to the digital product engineering space. 

 

Can you provide real-world examples of how businesses are benefiting from AI at the Edge today? 

Companies are seeing substantial benefits with the implementation of AI at the edge. Some of the key benefits are: 

  • Increasing work efficiency and customer satisfaction 
  • Reducing overall business costs 
  • Rapidly expanding and improving consumer insights with faster decision making 
  • Reducing the risk of cybersecurity attacks 

Self-driving cars, video surveillance cameras, Amazon's Alexa, Apple's Siri, Google Assistant, Raspberry Pi, image classifications, Apple's face recognition, health data analysis and preventive measures, etc. are some good examples of AI at the Edge devices. 

 

Who stands to benefit from AI at the Edge the most? 

Everyone benefits from the implementation of AI at the Edge. These technologies facilitate real-time data processing by ensuring high-performance data computation and analytics in IoT and other smart devices. Hence, the businesses who are in the development of hardware, firmware, and software of such devices will benefit from them. 

 The businesses that use and deploy such edge devices will benefit as well, as it will help them perform at a higher productivity and efficiency rate; while making informed decisions more quickly. 

 

How does AI at the Edge fit into larger IT and business initiatives? 

AI at the Edge has cemented its place in the broader analysis of topology within organizations. Adding a robust edge and near-edge components to an organization’s framework can lower bandwidth costs and reduce latency for mission-critical production applications. The combination of IoT edge devices, greater adoption of AI/ML, building more performance and computing power will become a game changer in the tech industry.  

 The key will be to develop an agile system that automates the collection, transformation and analysis of machine data and rapidly distributes it to relevant end users. This would be real-time data analytics and would assist faster decision-making to provide demonstrable ROI. 

 

What does AI at the Edge mean for privacy and compliance? 

AI at the Edge provides higher data security and privacy as compared to cloud deployments. The edge devices and the complete environment lie within the company network; hence, data privacy or General Data Protection Regulation (GDPR) compliance rules are not compromised at all. The business needs to build a strong access management and control process to take care of any security or vulnerability attacks as well.  

There are VPN software and utilities available, to further secure the network, depending on the kind of data the company is dealing with. In AI at the Edge, only part of the data is uploaded to the edge cloud or datacenters for training purposes, but that too can be anonymized to protect user identities (PII data). 

 

 

We sincerely thank Vivek Rastogi, Senior Vice President at Encora. The focus of this piece, AI at the Edge, is one of ten technology trends featured in Encora’s 2023 Technology Trends eBook. You can read the eBook in its entirety by visiting Encora’s 2023 Technology Trends. 

"Businesses will need to rely on AI at the Edge if they want to keep up with the fast-paced world and have Real-Time Data Analytics at their fingertips. This will help in bringing operational efficiency and increase productivity within businesses." 

Vivek Rastogi, Senior Vice President 

 

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About Encora 

Encora is a digital engineering services company specializing in next-generation software and digital product development. Fast-Growing Tech businesses trust Encora to lead the full Product Development Lifecycle because of our expertise in translating our clients’ strategic innovation roadmap into differentiated capabilities and accelerated bottom-line impacts. 

Please let us know if you would ever like to have a conversation with a client partner and/or one of our Innovation Leaders about accelerating next-generation product engineering within your organization. 

 

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