The progress of Artificial Intelligence (AI) is moving at an unprecedented pace, and the emergence of edge processing has given rise to a new form of AI, most commonly known as AI Edge Computing.
Scenera’s AI Topology Management (SATM) platform MAIstro not only integrates Traditional AI, but also AI Edge Computing to optimize real-time video data with AI analytics for latency, accuracy and cost, providing efficiencies for businesses across various industries and verticals. We will delve into the basics of what AI Edge Computing is and how it differs from Traditional AI, to better understand the full capabilities MAIstro offers.
What is AI Edge Computing?
AI Edge Computing refers to the deployment of AI algorithms and models at the edge of a network, where data is analyzed locally, rather than being sent to a central location for processing. AI Edge Computing brings processing power closer to the data source, allowing real-time analysis and decision-making. Edge devices are usually smaller, portable, and less powerful than traditional data centers, and are designed to handle limited processing power, memory, and battery life.
Scenera’s AI Edge Computing algorithms are optimized to overcome these limitations and are tailored to run on edge devices for specific use cases. Our models are trained using a combination of centralized and decentralized approaches to ensure accuracy, efficiency, and robustness.
How does this differ from Traditional AI?
Traditional AI is typically deployed on large-scale computing systems such as data centers, and trained using vast amounts of data collected from various sources. On the other hand, AI Edge Computing is deployed on edge devices with limited processing power and memory, and is trained using data collected from these devices. Traditional AI models are centralized, while AI Edge Computing is decentralized, allowing for real-time processing and analysis of data locally.
MAIstro: Scenera’s Solution
At Scenera, we have devised a solution to address the mounting concern of how to efficiently distribute artificial intelligence analytics in both Traditional AI and AI Edge Computing using the latest technological advancements in bandwidth, wireless accessibility, and network prioritization.
From an AI Edge Computing standpoint, Scenera’s comprehensive, fully customizable, and adaptive platform, MAIstro, provides several benefits, including reducing latency, bandwidth requirements, and costs by processing and analyzing data locally. It also allows for real-time processing, enabling immediate responses to events as they occur. Additionally, AI Edge Computing has significant implications for privacy and security, reducing the risk of sensitive data exposure or data breaches.
As AI becomes increasingly prevalent, the question is no longer which AI to employ but rather where to execute the appropriate AI utilizing 5G among different devices, on premise bridges, MEC (Multi-Edge Computing) carrier networks, and the cloud. Scenera addresses this issue by coordinating AI efficiently and effectively across all platforms to ensure it runs in the most optimal location for the best possible outcomes.
Contact us at email@example.com to schedule a live demo and customize Scenera’s MAIstro platform to fit your business needs.