Presented By O'Reilly and Cloudera
Make Data Work
December 1–3, 2015 • Singapore
Danielle Dean

Danielle Dean
Technical Director, Machine Learning, iRobot

Website | @danielleodean

Danielle Dean is the technical director of machine learning at iRobot. Previously, she was a principal data science lead at Microsoft. She holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill.

Sessions

1:30pm–5:00pm Tuesday, 12/01/2015
Data Science and Advanced Analytics
Location: 334 Level: Intermediate
Danielle Dean (iRobot), Wee Hyong Tok (Microsoft)
Average rating: ****.
(4.57, 7 ratings)
In this tutorial, you will create end-to-end predictive models based on an extensive library of machine learning algorithms included in Microsoft Azure Machine Learning studio with its R and Python language extensibility. You will then deploy and consume the model and use it for making predictions over business data. Read more.
4:00pm–4:40pm Wednesday, 12/02/2015
IoT and Real-time
Location: 324 Level: Intermediate
Tags: iot
Danielle Dean (iRobot)
Average rating: ****.
(4.50, 6 ratings)
Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. This talk introduces the landscape and challenges of predictive maintenance applications in the industry. Through a real-world example, the talk also illustrates how to formulate a predictive maintenance problem with three machine learning models. Read more.