Closing the Latency Gap: Real-Time Decision Making at the Point of Data Creation
The shift toward real-time decision-making at the edge is an evolution in how businesses operate. Closing the latency gap means smarter, safer, and more …
Topic: Edge Computing
How edge computing and edge analytics use real-time data for a variety of applications, including IoT.
Why Modern AI Needs NaaS
Enterprises and the providers delivering services to support AI efforts essentially need AI connectivity as a service. That’s where network-as-a-service …
Adaptive Edge Intelligence Meets 6G
For adaptive edge intelligence, 6G is not just an infrastructure upgrade, it provides an opportunity to reinvent how systems learn, decide, and
Beyond Latency: The Next Phase of Adaptive Edge Intelligence
The industry is now entering a phase where adaptive edge intelligence is less about the speed of a decision and more about
Why Adaptive Edge Intelligence Is the Future of Data-Driven Enterprises
While the vision for adaptive edge intelligence is clear, the execution requires technology that can combine real-time data processing, low-latency …
How Kafka and Edge Processing Enable Real-Time Decisions
By pairing Kafka with adaptive edge processing, organizations achieve the best of both worlds: instantaneous local decisions and enterprise-wide
Real-Time Decisions at the Edge: Adaptive Edge Intelligence Use Cases Across Industries
Discover how adaptive edge intelligence drives real-time fraud detection, network optimization, predictive maintenance, and anomaly detection across
Applications Abound for Industrial Edge AI
Shifting to edge AI and analytics in industrial operations reduces latency, improves responsiveness, and lowers the cost of backhauling data to centralized …
Why Intelligence at the Edge is No Longer Optional
By processing data locally, organizations can filter and act on the most important insights immediately, while sending only relevant or aggregated data …
Real-Time Visual AI at the Edge: Performance Without Compromise
Orchestrating a robotic intervention, flagging an anomaly, or executing a stop command on the production line requires a real-time, intelligent