Edge AI for Industrial Applications: Computer Vision, Robotics and RealTime

Industrial environments increasingly require intelligent systems that can process information quickly, respond to changing conditions and operate reliably without depending entirely on remote cloud infrastructure.

Edge AI enables artificial intelligence models to run on local devices, industrial computers and embedded platforms, bringing inference closer to sensors, cameras, machines and robotic systems.

For manufacturers and automation teams, Edge AI can support applications such as visual quality inspection, equipment monitoring, robotic perception and real-time operational decision support.

This NobleProg hybrid event explores the technologies, architectural considerations and deployment challenges involved in applying Edge AI to industrial computer vision, robotics and real-time inference.

Participants can attend onsite in Delhi, or join live online, with both attendance options running simultaneously.

Why Attend?

Industrial AI applications often operate under demanding requirements involving latency, computing resources, network connectivity and operational reliability.

This event explores how Edge AI can help organisations address these requirements.

Participants will examine:

• The role of Edge AI in industrial automation.
• Differences between cloud-based and edge-based AI inference.
• Computer vision applications for industrial inspection and monitoring.
• AI inference on embedded and edge computing platforms.
• Model optimisation for resource-constrained devices.
• Integration with sensors, cameras and robotic systems.
• Performance considerations involving latency, throughput and reliability.
• Security, monitoring and maintenance of deployed Edge AI applications.

Key Topics

1. Introduction to Edge AI

Explore the principles of running AI models closer to the physical systems that generate and use data.

Potential topics include:

• Edge AI architecture.
• Cloud AI versus edge inference.
• Industrial edge computing.
• Local processing and connectivity.
• Benefits and limitations of edge deployment.

2. Industrial Computer Vision

Examine how computer vision can support industrial applications.

Potential areas include:

• Image acquisition and preprocessing.
• Object detection and classification.
• Defect detection and visual inspection.
• Image segmentation.
• Camera integration.
• Environmental challenges in industrial vision systems.

3. Real-Time AI Inference

Explore how inference performance affects industrial applications.

Consider:

• Inference latency.
• Processing throughput.
• Resource utilisation.
• Deterministic timing requirements.
• Hardware acceleration.
• Performance benchmarking.

Real-time suitability depends on the complete system architecture and validated timing behaviour, not simply on whether inference runs locally.

4. Edge AI Hardware and Computing Platforms

Examine the characteristics of different edge computing environments, including:

• Industrial PCs.
• Embedded computing devices.
• GPU-accelerated edge systems.
• AI accelerators.
• Device memory and power constraints.
• Hardware selection considerations.

5. AI Model Optimisation for Edge Deployment

Explore techniques for adapting AI models to constrained computing environments.

Potential topics include:

• Model quantisation.
• Pruning and compression concepts.
• Inference graph optimisation.
• Model format compatibility.
• Hardware-specific acceleration.
• Balancing accuracy, latency and resource usage.

6. Edge AI for Industrial Robotics

Explore how local AI inference can support robotic applications.

Potential areas include:

• Robot perception.
• Object recognition and localisation.
• Vision-guided manipulation.
• Sensor-driven automation.
• Integration with robotic software.
• Coordination between perception and control systems.

7. Industrial Edge AI Architecture

Consider how Edge AI systems connect with industrial equipment and operational infrastructure.

Potential topics include:

• Camera and sensor data pipelines.
• Local inference services.
• Industrial networking.
• Edge-to-cloud communication.
• Integration with automation platforms.
• Data processing and event reporting.

8. Deployment, Monitoring and Maintenance

Explore considerations for operating AI models on industrial edge devices.

These may include:

• Model deployment and version management.
• Monitoring inference performance.
• Updating deployed models.
• Handling hardware limitations.
• Detecting model performance degradation.
• Managing connectivity interruptions.
• Maintaining operational reliability.

9. Security and Industrial Safety

Examine the importance of secure device configuration, access control, software updates and protection of industrial data.

Consider the distinction between AI-based perception or decision support and safety-rated industrial control systems.

10. Evaluating Industrial Edge AI Use Cases

Explore how organisations can assess potential applications based on:

• Business and operational requirements.
• Latency constraints.
• Data availability and quality.
• Hardware requirements.
• Model accuracy.
• Integration complexity.
• Deployment and maintenance costs.

These are proposed topic areas based on the event title. Final curriculum coverage, development frameworks, hardware platforms, technical demonstrations and practical exercises require confirmation against the approved course agenda.

Who Should Attend?

This event is relevant to:

• Industrial automation engineers
• AI and machine learning engineers
• Computer vision engineers
• Robotics engineers
• Embedded systems developers
• Manufacturing technology professionals
• Edge computing and IoT engineers
• Systems integration engineers
• Industrial IT and OT professionals
• Technical leaders evaluating industrial AI applications

Prerequisites

A general understanding of software development, AI concepts, computer vision or industrial automation may help participants engage with the technical content.

Specific programming, machine learning or hardware prerequisites will depend on the confirmed course agenda.

Delivery Format Hybrid — Onsite and Live Online

Participants can attend physically at the Delhi venue or participate remotely through live online delivery.

About NobleProg

NobleProg provides professional training in technical and business-related subjects, supporting individuals and organisations in developing relevant knowledge and skills.

Registration

Register your interest in Edge AI for Industrial Applications in Delhi.

Choose onsite participation in Delhi or live online attendance when the event schedule and registration options are confirmed. Contact us at training@nobleprog.in