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Optimizing ML at the Edge for Industrial IoT with Sastry Malladi of FogHorn | Utilizing AI: 2×16

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 cybersecurity, 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

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

Guests and Hosts

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About the author

Stephen Foskett

Stephen Foskett is an active participant in the world of enterprise information technology, currently focusing on enterprise storage, server virtualization, networking, and cloud computing. He organizes the popular Tech Field Day event series for Gestalt IT and runs Foskett Services. A long-time voice in the storage industry, Stephen has authored numerous articles for industry publications, and is a popular presenter at industry events. He can be found online at,, and on Twitter at @SFoskett.

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