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Podcast

Optimizing ML at the Edge for Industrial IoT with Sastry Malladi of FogHorn

Industrial cameras and sensors are generating more data than ever, and companies are increasingly moving machine learning to the edge to meet it

Industrial cameras and sensors are generating more data than ever, and companies are increasingly moving machine learning to the edge to meet it. This is the market for FogHorn, so we invited Co-Founder Sastry Malladi to join Chris Grundemann and Stephen Foskett to discuss the implications of this challenge. Industrial IoT, also called operational technology, is the use of distributed connected sensors and devices in industrial environments, from factories to oil rigs to retail. Any solution to this problem must be oriented towards the staff and skills found in these environments and must reflect the data inputs and outputs found there. Another concern is cyber security, since these environments are increasingly being targeted by attackers. Machine learning can be brought in to control industrial processes and monitor sensors locally, with low latency and high accuracy, reducing risk and increasing profitability. These environments also benefit from transfer learning, periodic re-training, and closed-loop machine learning to keep them optimized and functional

Three Questions

  • Is machine learning a product or a feature?
  • When will we have video-focused ML in the home that operates like the audio-based AI assistants like Siri or Alexa?
  • Are there any jobs that will be completely eliminated by AI in the next five years?

Guests and Hosts

Sastry Malladi, CTO and Co-Founder at FogHorn. Connect with Sastry on LinkedIn or on Twitter at @M_Sastry.

Chris Grundemann, Chris Grundemann, a Gigaom Analyst and Managing Director at Grundemann Technology Solutions. Connect with Chris on ChrisGrundemann.com on Twitter at @ChrisGrundemann.

Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.