Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK
Nick Pentreath

Nick Pentreath
Principal Engineer, IBM

Nick Pentreath is a principal engineer at the Center for Open Source Data & AI Technologies (CODAIT) at IBM, where he works on machine learning. Previously, he cofounded Graphflow, a machine learning startup focused on recommendations, and was at Goldman Sachs, Cognitive Match, and Mxit. He’s a committer and PMC member of the Apache Spark project and author of Machine Learning with Spark. Nick is passionate about combining commercial focus with machine learning and cutting-edge technology to build intelligent systems that learn from data to add business value.

Sessions

12:05–12:45 Wednesday, 23 May 2018
Deep learning for recommender systems
Data science and machine learning
 Location: Capital Suite 13 Level: Intermediate
Secondary topics:  E-commerce and Retail, Media, Advertising, Entertainment
Nick Pentreath (IBM)
Average rating: ****.
(4.43, 7 ratings)
In the last few years, deep learning has achieved significant success in a wide range of domains, including computer vision, artificial intelligence, speech, NLP, and reinforcement learning. However, deep learning in recommender systems has, until recently, received relatively little attention. Nick Pentreath explores recent advances in this area in both research and practice. Read more.
11:15–11:55 Thursday, 24 May 2018
Model parallelism in Spark ML cross-validation
Data science and machine learning
 Location: Capital Suite 10/11 Level: Beginner
Nick Pentreath (IBM), BRYAN CUTLER (IBM)
Average rating: **...
(2.50, 2 ratings)
Tuning a Spark ML model using cross-validation involves a computationally expensive search over a large parameter space. Nick Pentreath and Bryan Cutler explain how enabling Spark to evaluate models in parallel can significantly reduce the time to complete this process for large workloads and share best practices for choosing the right configuration to achieve optimal resource usage. Read more.