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

Schedule: Media, Advertising, Entertainment sessions

11:1511:55 Wednesday, 23 May 2018
Daniel Gilbert (News UK), Jonathan Leslie (Pivigo)
Average rating: ***..
(3.75, 4 ratings)
In the era of 24-hour news and online newspapers, editors in the newsroom must quickly and efficiently make sense of the enormous amounts of data that they encounter and make decisions about their content. Daniel Gilbert and Jonathan Leslie discuss an ongoing partnership between News UK and Pivigo in which a team of data science trainees helped develop an AI platform to assist in this task. Read more.
11:1511:55 Wednesday, 23 May 2018
Data engineering and architecture
Location: S11B Level: Intermediate
Jason Heo (Naver), Dooyong Kim (Navercorp)
Average rating: ***..
(3.00, 1 rating)
Naver.com is the largest search engine in Korea, with a 70% share of the Korean search market, and it handles billions of pages and events everyday. Jason Heo and Dooyong Kim offer an overview of Naver's web analytics system, built with Druid. Read more.
11:1511:55 Wednesday, 23 May 2018
Guillaume Chaslot (AlgoTransparency)
Average rating: ****.
(4.17, 6 ratings)
An increasing number of ex-Google and ex-Facebook employees state that social media is starting to control us rather than the other way around. How can we determine if social media is a pure reflection of people's interests or if it pushes us toward specific narratives? Guillaume Chaslot explores methodologies to find out which narratives are favored by social media recommendation engines. Read more.
12:0512:45 Wednesday, 23 May 2018
Data science and machine learning
Location: Capital Suite 13 Level: Intermediate
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.
12:0512:45 Wednesday, 23 May 2018
Data science and machine learning
Location: Capital Suite 12
Elisa Celis (EPFL)
Average rating: ****.
(4.25, 4 ratings)
There is a pressing need to design new algorithms that are socially responsible in how they learn and socially optimal in the manner in which they use information. Elisa Celis explores the emergence of bias in algorithmic decision making and presents first steps toward developing a systematic framework to control biases in classical problems, such as data summarization and personalization. Read more.
16:3517:15 Wednesday, 23 May 2018
Big data and data science in the cloud, Data science and machine learning
Location: Capital Suite 13 Level: Intermediate
Sergey Ermolin (Intel), Olga Ermolin (MLS Listings)
Average rating: ****.
(4.00, 1 rating)
Aggregation of geospecific real estate databases results in duplicate entries for properties located near geographical boundaries. Sergey Ermolin and Olga Ermolin detail an approach for identifying duplicate entries via the analysis of images that accompany real estate listings that leverages a transfer learning Siamese architecture based on VGG-16 CNN topology. Read more.
11:1511:55 Thursday, 24 May 2018
Data engineering and architecture
Location: S11B Level: Intermediate
Irene Gonzálvez (Spotify)
Average rating: ***..
(3.88, 8 ratings)
Irene Gonzálvez shares Spotify's process for ensuring data quality, covering why and how the company became aware of its importance, the products it has developed, and future strategy. Read more.
11:1511:55 Thursday, 24 May 2018
Kinnary Jangla (Pinterest)
Average rating: ***..
(3.00, 5 ratings)
Having trouble coordinating development of your production ML system between a team of developers? Microservices drifting and causing problems debugging? Kinnary Jangla explains how Pinterest dockerized the services powering its home feed and how it impacted the engineering productivity of its ML teams while increasing uptime and ease of deployment. Read more.