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March 24: Reducing Costs with Predictive Maintenance

Community Team
Community Team
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(Part of a modeling building learning session series.)

Disruptions caused by unexpected equipment failures are estimated to cost manufactures billions of dollars a year in the United States alone. Machine learning models trained on emerging Internet of Things  (IoT) data provide an opportunity to cut back on losses resulting from unplanned downtime, delayed production, and equipment replacement costs.

Join Dave Heinicke, a Customer Facing Data Scientist, as he covers:

  • How to identify a machine learning opportunity to reduce equipment costs.
  • How to frame the machine learning problem and identify a target.
  • Successful approaches to partitioning your data and training and deploying a predictive maintenance model using DataRobot.


  • Dave Heinicke (DataRobot, Customer Facing Data Scientist)
  • Kamesh Vemu (DataRobot, AI Success Director)
  • Reagan Yarema (DataRobot, AI Success)

Now what?

After watching the learning session, you should check out these resources for more information.

DataRobot Community:

Artificial Intelligence wiki: Data Insights
DataRobot licensed customers: search in-app documentation for Anomaly detection or Unsupervised learning (anomaly detection).


Also, if you have comments or questions that weren't answered in the learning session, click Comment (below) and post them now.

Looking for live, in-person classes? See details: DataRobot University
About the Author
Linda is Senior Technical Writer for DataRobot Community.