Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

Schedule: Media, entertainment, and advertising sessions

Explore how new data science, machine learning, and big data approaches are transforming entertainment, media, and ad tech.

9:00am–5:00pm Tuesday, March 6, 2018
Data Case Studies
Location: LL20 A
Madhav Madaboosi (BP), Meenakshisundaram Thandavarayan (Infosys), Matt Conners (Microsoft), Katie Malone (Civis Analytics), Mike Prorock (mesur.io), Thomas Miller (Northwestern University), Ann Nguyen (Whole Whale), Jennie Shin (Kaiser Permanente), Valentin Bercovici (PencilDATA), Wayde Fleener (General Mills), Joe Dumoulin (Next IT), Jules Malin (GoPro), Taylor Martin Martin (O'Reilly Media), Divya Ramachandran (Captricity)
Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions. Read more.
9:00am–5:00pm Tuesday, March 6, 2018
Media and Ad Tech Day
Location: LL20 B
David Boyle (Audience Strategies), Violeta Hennessey (Warner Bros.), April Chen (Civis Analytics), Sridhar Alla (BlueWhale), Noah Gift (UC Davis), Blake Irvine (Netflix), Kevin Lyons (Nielsen Marketing Cloud), Jennifer Webb (SuprFanz), Rizwan Patel (Caesars Entertainment), Anthony Accardo (Disney), Amanda Gerdes (Blizzard Entertainment), Violeta Hennessey (Warner Bros.), Aneesh Karve (Quilt), David Boyle (Audience Strategies), Pete Skomoroch (Workday)
Hear from innovators in ad tech, measurement, automation, and audience engagement about where the media industry is today—and where it's likely to go next. Read more.
1:30pm–5:00pm Tuesday, March 6, 2018
Deep learning-based search and recommendation systems using TensorFlow
Location: LL21 E/F
Abhishek Kumar (Publicis Sapient), Vijay Agneeswaran (Walmart Labs)
Average rating: ****.
(4.00, 3 ratings)
Abhishek Kumar and Vijay Srinivas Agneeswaran offer an introduction to deep learning-based recommendation and learning-to-rank systems using TensorFlow. You'll learn how to build a recommender system based on intent prediction using deep learning that is based on a real-world implementation for an ecommerce client. Read more.
11:50am–12:30pm Wednesday, March 7, 2018
Machine learning versus machine learning in production
Location: LL21 E/F
Manu Mukerji (8x8)
Average rating: ****.
(4.22, 9 ratings)
Acme Corporation is a global leader in commerce marketing. Manu Mukerji walks you through Acme Corporation's machine learning example for universal catalogs, explaining how the training and test sets are generated and annotated; how the model is pushed to production, automatically evaluated, and used; production issues that arise when applying ML at scale in production; lessons learned; and more. Read more.
9:10am–9:30am Thursday, March 8, 2018
Differentiating via data science
Location: Grand Ballroom 220
Eric Colson (Stitch Fix)
Average rating: ****.
(4.50, 10 ratings)
While companies often use data science as a supportive function, the emergence of new business models has made it possible for some companies to differentiate via data science. Eric Colson explores what it means to differentiate by data science and explains why companies must now think very differently about the role and placement of data science in the organization. Read more.
11:50am–12:30pm Thursday, March 8, 2018
Hive as a service
Location: LL21 E/F
Szehon Ho (Criteo), Pawel Szostek (Criteo)
Average rating: ****.
(4.50, 2 ratings)
Hive is the main data transformation tool at Criteo, and hundreds of analysts and thousands of automated jobs run Hive queries every day. Szehon Ho and Pawel Szostek discuss the evolution of Criteo's Hive platform from an error-prone add-on installed on some spare machines to a best-in-class installation capable of self-healing and automatically scaling to handle its growing load. Read more.