Organizations are facing increasing complexity in processing and acting on data due to the growth of edge computing, advancements in AI, and open source platforms. Edge devices generate massive amounts of data, requiring advanced analytical tools and AI models for insight. While AI models are becoming more efficient for real-time analytics at data generation points, organizations adopting AI at the edge encounter challenges such as governing AI workloads across distributed locations, operationalizing models in disconnected environments, a shortage of MLOps engineers at the edge, and inadequate data pipelines often due to proprietary systems. To effectively utilize AI at the edge, organizations need to adopt a modern, open approach that includes AI inferencing at the edge for real-time decision-making, scalable and secure deployments across hybrid environments, and interoperable platforms that unify IT and operational technology.
