Validating a Jet Engine Predictive Model in a Cloud Environment

In this meetup, we’ll go through the process of applying machine learning concepts to create a predictive model and validate its cost effectiveness on a publically available NASA dataset that shows how jet engines degrade over time. Using python within a modern cloud platform, we’ll create a gradient boosted regression model using XGBoost, an open-source library, and layer a classifier on top in order to predict when an engine should be expected to require maintenance. To determine cost effectiveness, we’ll assign dollar values to the classifier’s potential outcomes and determine the fiscal impact of implementing this predictive model. We’ll show how predictive models can save lives as well as save companies huge sums of money.

Come join us to see the process we’ve created and hopefully it will inspire some new ways of thinking!

Agenda
11:00 am: Informal Introductions and Announcements
11:10 am: Validating a Predictive Model in a Cloud Environment presentation
12:00 pm: Q&A
12:20 pm: Raffle of door prizes
12:25 pm Preview of upcoming Meetups, concluding remarks

For a preview of the content we'll be covering, we've got the following resources:

Video:
https://youtu.be/JzPHzz8mdIg

Blog:
https://blog.cloudera.com/using-cml-to-build-a-predictive-maintenance-model-for-jet-engines

Tutorial:
https://www.cloudera.com/tutorials/cml-validate-jet-engine-predictive-models.html

Cloudera Users Page:
https://www.cloudera.com/users.html

Due to the ongoing nature of the new corona virus pandemic, this will be an online event. Use the hyperlink provided to participants upon registration to view and interact with the "live stream".