Reducing Costs with Predictive Maintenance
This post was originally part of the DataRobot Community. Visit now to browse discussions and ask questions about the DataRobot AI Platform, data science, and more.
Disruptions caused by unexpected equipment failures are estimated to cost manufacturers 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.
During this session, we discuss:
- 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.
Host
- Dave Heinicke (DataRobot, Customer Facing Data Scientist)
Now what?
After watching the learning session, you should check out these resources for more information.
Artificial Intelligence wiki: Data Insights
Platform Documentation:
We will contact you shortly
We’re almost there! These are the next steps:
- Look out for an email from DataRobot with a subject line: Your Subscription Confirmation.
- Click the confirmation link to approve your consent.
- Done! You have now opted to receive communications about DataRobot’s products and services.
Didn’t receive the email? Please make sure to check your spam or junk folders.
-
Harnessing Synthetic Data for Model Training
September 28, 2023· 5 min read -
Introducing the Latest DataRobot Integrations with Google Cloud, Including a New Generative AI Initiative
September 21, 2023· 4 min read -
DataRobot AI Production: Unifying MLOps and LLMOps
September 14, 2023· 7 min read
Latest posts
