Industrial sectors are experiencing a pivotal shift as AI technology transitions from traditional centralized analytics to sophisticated, real-time edge deployment. This masterclass delves into the technical intricacies of designing and deploying resilient AI systems tailored for mission-critical environments. Learn how these advanced solutions can significantly enhance operational uptime, ensure safety protocols, and optimize efficiency across manufacturing, energy, and logistics sectors.

What You’ll Learn

• Architecting AI pipelines for predictive maintenance, quality control, and real-time anomaly detection in industrial systems.
• Integrating AI with SCADA, PLCs, and IoT sensor networks for closed-loop control and decision-making
• Deploying edge-native AI models using lightweight frameworks (e.g., TensorRT, ONNX, TinyML) for latency-sensitive environments.

Key Takeaways

• Robust reference architectures are essential for effective AI-enabled industrial automation and digital twins.
• Comprehensive deployment templates and governance frameworks are critical for ensuring safety and compliance in mission-critical AI systems within regulated industrial environments.

Why This Matters

Industrial operations demand reliability, speed, and precision. AI at the edge enables real-time decision-making where milliseconds matter—reducing downtime, improving safety, and unlocking new efficiencies. This session equips technical leaders with the tools to build intelligent, resilient, and scalable automation systems.

Who Should Attend

AI engineers, automation architects, IoT specialists, and innovation teams working in:

• Manufacturing
• Energy & Utilities
• Transportation & Logistics
• Smart Infrastructure