Louis Columbus: Realizing IoT’s potential with AI and machine learning
August 14, 2021
The key to getting more value from industrial internet of things (IIoT) and IoT platforms is getting AI and machine learning (ML) workloads right. Despite the massive amount of IoT data captured, organizations are falling short of their enterprise performance management goals because AI and ML aren’t scaling for the real-time challenges organizations face. If you solve the challenge of AI and ML workload scaling right from the start, IIoT and IoT platforms can deliver on the promise of improving operational performance.
Overcoming IoT’s growth challenges
More organizations are pursuing edge AI-based initiatives to turn IoT’s real-time production and process monitoring data into results faster. Enterprises adopting IIoT and IoT are dealing with the challenges of moving the massive amount of integrated data to a datacenter or centralized cloud platform for analysis and derive recommendations using AI and ML models. The combination of higher costs for expanded datacenter or cloud storage, bandwidth limitations, and increased privacy requirements are making edge AI-based implementations one of the most common strategies for overcoming IoT’s growth challenges.