2026-02-14 –, VYAS - G - Room#VY003
Edge AI brings machine learning inference directly to devices, enabling real-time processing without cloud dependency. This architecture reduces latency from hundreds of milliseconds to microseconds, enhances privacy by keeping data local, cuts bandwidth costs by 60-80%, and enables offline operation. Applications include smart traffic management with dynamic signal timing, industrial predictive maintenance, autonomous vehicles, healthcare wearables with real-time monitoring, and smart home energy optimization. The combination of TinyML (machine learning on microcontrollers), neuromorphic chips, and 5G connectivity is making edge AI increasingly practical.
- Real-Time Processing at the Edge
- Latency Reduction: From Milliseconds to Microseconds
- Enhanced Privacy and Security
- Cost Savings: Reduced Bandwidth Usage by 60-80%
- Offline Capabilities for Seamless Operation
The TechTalk will cover that Edge AI is not just a technological trend—it’s transforming industries by providing faster, cheaper, and more secure ways of processing data. With the combination of emerging technologies like TinyML, neuromorphic chips, and 5G, it’s reshaping everything from healthcare to smart cities, and laying the groundwork for an autonomous future.
Deepak Das, has been working in Red Hat for the last 12+ year. Has total of around 25 years of IT experience.