Presented By O'Reilly and Cloudera
Make Data Work
March 13–14, 2017: Training
March 14–16, 2017: Tutorials & Conference
San Jose, CA
Valentine Fontama

Valentine Fontama
Principal Data Scientist Manager, Microsoft

Valentine Fontama is a principal data scientist manager on Microsoft’s Analytics + Insights Data Science team that delivers analytics capabilities across Azure and C+E cloud services. Previously, he was a new technology consultant at Equifax in London, where he pioneered the use of data mining to improve risk assessment and marketing in the consumer credit industry; principal data scientist in the Data & Decision Sciences Group (DDSG), where he led consulting to external customers, including ThyssenKrupp and Dell; and a senior product manager for big data and predictive analytics in cloud and enterprise marketing at Microsoft, where he led product management for Azure Machine Learning, HDInsight, Parallel Data Warehouse (Microsoft’s first ever data warehouse appliance), and three releases of Fast Track Data Warehouse. He has published 11 academic papers and coauthored three books on big data: Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes (2 editions) and Introducing Microsoft Azure HDInsight. Val holds an MBA in strategic management and marketing from the Wharton School, a PhD in neural networks, an MS in computing, and a BS in mathematics and electronics.

Sessions

11:00am11:40am Wednesday, March 15, 2017
Data science & advanced analytics
Location: 210 C/G Level: Intermediate
Secondary topics:  Deep learning, ecommerce, Retail
Feng Zhu (Clobotics), Valentine Fontama (Microsoft)
Average rating: ****.
(4.71, 7 ratings)
Although deep learning has proved to be very powerful, few results are reported on its application to business-focused problems. Feng Zhu and Val Fontama explore how Microsoft built a deep learning-based churn predictive model and demonstrate how to explain the predictions using LIME—a novel algorithm published in KDD 2016—to make the black box models more transparent and accessible. Read more.