Getting Started: Understanding Edge Infrastructure requirements

One of the most important and costly expenses when rolling out an edge AI solution is infrastructure. Unlike data centers, edge computing infrastructure must take into additional considerations around performance, bandwidth, latency, and security.

Start by looking at the existing infrastructure to understand what is already in place and what needs to be added. Here are some of the infrastructure items to consider for your edge AI platform.

1. Sensors: Most organizations today are relying on cameras as the main edge devices, but sensors can include chatbots, radar, lidar, temperature sensors, and more.

2. Edge nodes/ Compute systems: When sizing compute systems, consider the performance of the application and the limitations at the edge location, including space, power constraints, and heat. When these limiting factors are determined, you can then understand the performance requirements of your application.

3. Network: The main consideration for networking is how fast of a response you need for the use case to be viable, or how much data and whether real-time data must be transported across the network. Due to latency and reliability, wired networks are used where possible, though wireless is an option when needed.

4. Edge Management: Edge computing presents unique challenges in the management of these environments. Organizations should consider solutions that solve the needs of edge AI, namely scalability, performance, remote management, resilience, and security.

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Key considerations for technology leaders navigating the edge AI landscape

1. INFRASTRUCTURE COMPABILITY

1.1 Legacy System Integration: Many manufacturing plants operate with legacy systems that may not be directly compatible with the latest Edge AI technologies. Retrofitting these systems to communicate with modern edge devices without disrupting ongoing operations is a technical challenge.

1.2 Network Architecture: Implementing Edge AI requires a robust network architecture capable of handling increased data flow and ensuring real-time communication between edge devices and central servers. Designing a network that minimizes latency and maximizes reliability is crucial, especially in facilities spread across multiple locations.

2. DATA MANAGEMENTE AND PROCESSING

2.1 Volume and Velocity: Edge computing involves processing vast amounts of data generated in real-time by various sensors and devices. Managing this data volume and velocity, ensuring timely processing and analysis, poses significant technical challenges.

2.2. Data Quality and Standardization: The heterogeneity of data sources in manufacturing can lead to inconsistencies in data quality and format. Standardizing this data for effective processing and analysis by AI models requires sophisticated data management strategies.

2.3 Edge device  / Edge Node: At the heart of Edge AI are the devices which end up running the models. They all have different architectures, features and dependencies. Ensure that the capabilities of your hardware align with the requirements of your AI model, and ensure that the software – such as the operating system – is certified on the edge device..

3. SECURITY AND PRIVACY

3.1 Vulnerability to Attacks: With the decentralization of data processing, each edge device potentially becomes a target for cyberattacks. Ensuring the security of these devices and the data they process is a complex task that requires advanced cybersecurity measures.

3.2 Data Privacy: Localized processing of sensitive data is often critical for edge AI applications. Robust security measures, including encryption, access controls and persistent resource validation, are imperative to safeguard against potential threats. Hence, adopting zero-trust security framework is becoming critically important for Edge AI.

4. SCALABILITY AND MAINTENANCE

4.1 Scalable Deployment: As manufacturers look to deploy Edge AI across multiple locations, ensuring scalable deployment that can be easily managed and updated poses technical challenges. This includes the ability to remotely deploy updates or new models without causing disruptions. Flexibility in deployment across diverse use cases is crucial for long-term success.

4.2 On-going Maintenance: Edge devices and AI models require continuous monitoring and maintenance to ensure optimal performance. Developing a system for real-time monitoring, troubleshooting, and updating devices and models across multiple locations demands a sophisticated technical infrastructure. Managing and orchestrating a large number of distributed edge computing nodes with zero touch is a critical edge computing requirement.

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Barbara Platform: Deploying AI Across the Edge

Barbara is at the forefront of the AI Revolution. With cybersecurity at heart, Barbara helps organizations manage and orchestrate large number of distribuited edge computing nodes.

‍Barbara is the Edge AI Platform for organizations seeking to overcome the challenges of deploying AI, in mission-critical environments. With Barbara companies can deploy, train and maintain their models across thousands of devices in an easy fashion, with the autonomy, privacy and real-time that the cloud can´t match.

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Barbara´s technology stack is composed of:

• Industrial Connectors to attach edge devices to any other legacy or next-generation equipment.

• Batch Orchestration  to deploy and control container-based and native edge apps across thousands of distributed locations.

• Device Management to provision, configure, update, operate and decommission Edge Devices cybersecurely.

• MLOps to optimize and package your trained model in minutes.

• Marketplace of containerized Edge applications ready to be deployed. The marketplace includes third-party applications and a suite of Barbara microservices.

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Start deploying your apps and AI models in the Edge. Discover more about Barbara Edge Platform. Get access to a free trial now

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