Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY

Schedule: ecommerce sessions

9:00am12:30pm Tuesday, September 26, 2017
Artificial Intelligence, Machine Learning & Data Science
Location: 1A 18 Level: Intermediate
Mo Patel (Independent), Junxia Li (Think Big Analytics)
Junxia Li and Mo Patel demonstrate how to apply deep learning to improve consumer recommendations by training neural nets to learn categories of interest for recommendations using embeddings. You'll also learn how to achieve wide and deep learning with WALS matrix factorization—now used in production for the Google Play store. Read more.
9:00am5:00pm Tuesday, September 26, 2017
Location: 1A 06/07
Ben Lorica (O'Reilly), Assaf Araki (Intel), Jacob Schreiber (University of Washington), Alex Ratner (Stanford University), Madeleine Udell (Cornell University), Yunsong Guo (Pinterest), Katherine Heller (Duke University), Alan Nichol (Rasa), Gerard de Melo (Rutgers University), Tamara Broderick (MIT), Inbal Tadeski (Anodot), Daniel Kang (Stanford University), Bichen Wu (UC Berkeley), Shaked Shammah (Hebrew University)
A full day of hardcore data science, exploring emerging topics and new areas of study made possible by vast troves of raw data and cutting-edge architectures for analyzing and exploring information. Along the way, leading data science practitioners teach new techniques and technologies to add to your data science toolbox. Read more.
11:20am12:00pm Wednesday, September 27, 2017
Data engineering, Data Engineering & Architecture
Location: 1A 15/16/17 Level: Intermediate
Neelesh Salian (Stitch Fix)
Average rating: ***..
(3.00, 1 rating)
Neelesh Srinivas Salian offers an overview of the data platform used by data scientists at Stitch Fix, based on the Spark ecosystem. Neelesh explains the development process and shares some lessons learned along the way. Read more.
11:20am12:00pm Wednesday, September 27, 2017
Machine Learning & Data Science
Location: 1A 12/14 Level: Intermediate
Mikio Braun (Zalando)
Average rating: ***..
(3.71, 7 ratings)
Deep learning has become the go-to solution for many application areas, such as image classification or speech processing, but does it work for all application areas? Mikio Braun offers background on deep learning and shares his practical experience working with these exciting technologies. Read more.
2:55pm3:35pm Wednesday, September 27, 2017
Brian Dalessandro (Capital One)
Average rating: ****.
(4.67, 3 ratings)
Zocdoc is an online marketplace that allows easy doctor discovery and instant online booking. However, dealing with healthcare involves many constraints and challenges that render standard approaches to common problems infeasible. Brian Dalessandro surveys the various machine learning problems Zocdoc has faced and shares the data, legal, and ethical constraints that shape its solution space. Read more.
11:20am12:00pm Thursday, September 28, 2017
Data science & advanced analytics, Machine Learning & Data Science
Location: 1A 12/14 Level: Intermediate
Average rating: *****
(5.00, 1 rating)
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.
2:05pm2:45pm Thursday, September 28, 2017
Javier Esplugas (DHL Supply Chain), Kevin Parent (Conduce)
DHL has created an IoT initiative for its supply chain warehouse operations. Javier Esplugas and Kevin Parent explain how DHL has gained unprecedented insight—from the most comprehensive global view across all locations to a unique data feed from a single sensor—to see, understand, and act on everything that occurs in its warehouses with immersive operational data visualization. Read more.