Presented By O'Reilly and Cloudera
Make Data Work
September 26–27, 2016: Training
September 27–29, 2016: Tutorials & Conference
New York, NY
Shaheen Gauher

Shaheen Gauher
Microsoft, Microsoft

Shaheen Gauher is a data scientist in information management and machine learning at Microsoft, where she develops end-to-end, data-driven advanced analytics solutions for external customers. She is passionate about data and science and uses machine learning to come up with key insights that generate value for better decisions and better business performance. A climate scientist by training, Shaheen received her PhD in earth, ocean, and atmospheric sciences with a focus on satellite retrievals.

Sessions

2:55pm–3:35pm Thursday, 09/29/2016
Evaluating models for a needle in a haystack: Applications in predictive maintenance
Data science & advanced analytics
 Location: Hall 1C Level: Intermediate
Tags: iot, smart_cities, manufacturing
Danielle Dean (iRobot), Shaheen Gauher (Microsoft)
Average rating: ****.
(4.20, 5 ratings)
In the realm of predictive maintenance, the event of interest is an equipment failure. In real scenarios, this is usually a rare event. Unless the data collection has been taking place over a long period of time, the data will have very few of these events or, in the worst case, none at all. Danielle Dean and Shaheen Gauher discuss the various ways of building and evaluating models for such data. Read more.