*NEW RESEARCH* Edge AI: Pushing Workload Boundaries

Edge computing implementations are starting to gain momentum though the majority of organisations are still looking to identify potential use cases and better understand how the architectural approach can improve their application delivery, data processing and workflow automation. Some companies are already testing the water with small scale pilot projects before applying the technology elsewhere, particularly when it comes to supporting workloads with high performance requirements that use artificial intelligence (AI) and machine learning (ML) technology.

The larger and more complex the AI/ML data set involved, the harder applications have to work to ingest, process and analyse the information. That puts considerable strain on the underlying network, storage and server architecture which shifts data from one place to another for analysis, a situation that often creates latency and bandwidth bottlenecks that can both undermine application performance and compromise security and regulatory compliance policies.

TechMarketView’s latest Edge AI: Pushing Workload Boundaries research discusses how and where edge compute architectures and approaches can help to solve those performance issues by processing data in distributed locations rather than centralised data centres.

Subscribers to TechSectorViews can read more detail in our Edge AI: Pushing Workload Boundaries report here. If you don’t have a subscription and would like to know more about how to access our services, please email Deb Seth for more information.

Posted by Martin Courtney at '09:36' - Tagged: AI   ML   EdgeComputing   networkinfrastructure