• Connectivity: Provides many kinds of connectivity out to the edge sensors, whether they are IoT sensors, cameras or other devices.
  • Ingestion: Device data is ingested into the edge compute, on-premise server, or MEC
  • Enrichment: After ingestion, data is enriched, either at or near the edge, or in the cloud. Enrichment adds value to the data, whether it is AI-based video analytics or hunting for patterns in the IoT data streams.
  • Insights: Enrichment provides actionable insights, such as a vehicle having a near miss with a pedestrian to indicate an unsafe street corner, or that a silo is filling up quickly and will need to be emptied in three days.
  • Outcomes: This is why DeepInsights exist, to arm the public sector or enterprises with critical information to provide a safer, more secure or more connected world.

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DeepInsights was built with three core aspects in mind: refined AI video analytics models, accelerated speeds of deployment of all sizes of systems, and new key components now being used in DeepInsights Dashboards to explicitly deliver insights, not just alerts. DeepInsights can combine data from any source, apply logic and workflows, and deliver insights in real time.

With the scalability of DeepInsights and the flexibility of the AI, integrations to IoT sensors and other systems are enabling customers to utilize the platform to build and scale their projects and solutions over time. The deep learning algorithms make the connection of new or existing cameras to the edge compute device possible, expanding the strength and effectiveness of the system. Further, with transferring collected data to either the cloud or an on-premises server, strong processing power and long-term analysis is also made possible.

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[To share your insights with us, please write to sghosh@martechseries.com]