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Webinar: "Using Open Source for Failure Prediction"

Spread awareness of open source languages and tools within the data science and data-centric community.

When and where

Date and time

Location

Online

About this event

  • 1 hour
  • Mobile eTicket

Failure prediction in real time on time series data can be realized with the use of Open Source tools. We will deliver an overall view of how to start with the generation of new raw sensor data (typically captured by an Edge device), and end up with a real time graph that shows alerts warning that a failure is imminent. There are a number of processes that must be put into place before the stated goal can be fully realized.

In particular, we will first need a data collector whose job is to receive the raw sensor data and then put that data into a data storage unit. All of the new raw sensor data are associated with the timestamp of when the sensor data point was generated, thereby forming what is called a time series. The data collector then, by socket communication sends the new data to a web application that puts the new data into a form that enables a trained Machine Learning model to make a binary classification (Normal or Not Normal) prediction. A real time time series graph is then updated with the prediction, and the data is pushed to a browser where the real time graph is rendered. All of the processes mentioned above can be implemented with the use of Open Source tooling.

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