Training with historical data! Surely, you’re joking says the IoT asset that just got connected


  • Priya Sharma – Sr. Data Scientist -IoT Analytics, SAS Institute Inc.
  • Saurabh Mishra – Product Management, IoT, SAS Institute Inc.

June 12, 2020

Description: Majority of AI approaches are based on the construct of training against historical data and then inferencing new data. While this is a sound and proven approach, a lot of IoT assets coming online don’t have historical data and we don’t necessarily have the time to wait.

Modern Machine Learning methods can be employed to understand the behavior of newly connected IoT assets as soon as they are connected. This allows organizations to begin “condition-based monitoring” for these assets while they collect enough historical data to begin creating predictive models. Condition based monitoring can be used to support use cases such as early detection of performance degradation, emerging safety issues etc. which are especially relevant in Industrial IoT.


Although we may be talking about billions of connected devices and hundreds of IoT platforms, the reality remains that only a small percentage of Industrial assets are connected. According to McKinsey, only 15% of industrial assets in production environment are connected. Now this number is increasing for sure but the data being collected from these


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