Hardware Podcasts

Anomaly Detection with IoT | Ira Cohen, Founder & Chief Data Scientist, Anodot | #IoTFounders E026

IoT For All Podcast · 8 May 2019 · 43m

AI in practice: one real use of AI described

What this episode covers

Ira Cohen, co-founder and Chief Data Scientist of Anodot, explains how his company uses autonomous machine learning to detect anomalies in time-series data for Industrial IoT and business monitoring. He details the shift from static thresholds to dynamic pattern recognition, citing use cases in automotive manufacturing and large-scale printing to illustrate predictive maintenance and quality control. Cohen also discusses the challenges of implementing AI analytics at scale, including the need for organizational process changes and the importance of user-friendly interfaces for non-technical executives.

AI in practice

Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.

ManufacturingAnodot

Anodot monitored print head anomalies in large-scale printing presses to predict component failure, enabling the company to schedule maintenance proactively and minimize unplanned downtime.

“The use case there is proactive maintenance. … to actually, uh, uh, detect anomalies in the print heads on those uh, presses, uh, that could be indicators that they're going to go bad, and then schedule when to stop the press, uh, when there are enough of those, and you need to replace ink.”

Speaker 2 · Hear it at 31:58

Most relevant to

companies scaling up

Topics

Mentioned

Companies
Anodot, Uber, Get Taxi
Also
University of Illinois

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