Presented By
O’Reilly + Cloudera
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
San Francisco, CA
Schedule: Retail and e-commerce sessions
9:00am–5:00pm Tuesday, March 26, 2019
Location: 2022
Alex Kudriashova (Astro Digital),
Jonathan Francis (Starbucks),
JoLynn Lavin (General Mills),
Robin Way (Corios),
June Andrews (GE),
Kyungtaak Noh (SK Telecom),
Taposh DuttaRoy (Kaiser Permanente),
Sabrina Dahlgren (Kaiser Permanente),
Craig Rowley (Columbia Sportswear),
Ambal Balakrishnan (IBM),
Benjamin Glicksberg (UCSF),
Patrick Lucey (Stats Perform),
Rhonda Textor (True Fit)
Hear practical insights from household brands and global companies: the challenges they tackled, approaches they took, and the benefits—and drawbacks—of their solutions.
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11:00am–11:40am Wednesday, March 27, 2019
Average rating:
(4.50, 4 ratings)
Jian Chang and Sanjian Chen outline the design of the AI engine on Alibaba's TSDB service, which enables fast and complex analytics of large-scale retail data. They then share a successful case study of the Fresh Hema Supermarket, a major “new retail” platform operated by Alibaba Group, highlighting solutions to the major technical challenges in data cleaning, storage, and processing.
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11:50am–12:30pm Wednesday, March 27, 2019
Melinda Han Williams (Dstillery)
Average rating:
(4.86, 14 ratings)
Customer segmentation based on coarse survey data is a staple of traditional market research. Melinda Han Williams explains how Dstillery uses neural networks to model the digital pathways of 100M consumers and uses the resulting embedding space to cluster customer populations into fine-grained behavioral segments and inform smarter consumer insights—in the process, creating a map of the internet.
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11:50am–12:30pm Wednesday, March 27, 2019
Ron Bodkin (Google)
Average rating:
(4.33, 6 ratings)
Google uses deep learning extensively in new and existing products. Join Ron Bodkin to learn how Google has used deep learning for recommendations at YouTube, in the Play store, and for customers in Google Cloud. You'll explore the role of embeddings, recurrent networks, contextual variables, and wide and deep learning and discover how to do candidate generation and ranking with deep learning.
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4:20pm–5:00pm Wednesday, March 27, 2019
Average rating:
(4.00, 1 rating)
After two years of running streaming pipelines through Kinesis and Spark at One Click Retail, Jowanza Joseph and Karthik Ramasamy decided to explore a new platform that would take advantage of Kubernetes and support a simpler data processing DSL. Join in to discover why they chose Apache Pulsar and learn tips and tricks for using Pulsar Functions.
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4:20pm–5:00pm Wednesday, March 27, 2019
Luyang Wang (Restaurant Brands International),
Jing (Nicole) Kong (Office Depot),
Guoqiong Song (Intel),
Maneesha Bhalla (Office Depot)
Average rating:
(4.00, 2 ratings)
User-based real-time recommendation systems have become an important topic in ecommerce. Lu Wang, Nicole Kong, Guoqiong Song, and Maneesha Bhalla demonstrate how to build deep learning algorithms using Analytics Zoo with BigDL on Apache Spark and create an end-to-end system to serve real-time product recommendations.
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11:00am–11:40am Thursday, March 28, 2019
Average rating:
(5.00, 4 ratings)
JD.com recently designed a brand-new architecture to optimize Spark computing clusters. Yue Li and Shouwei Chen detail the problems the team faced when building it and explain how the company benefits from the in-memory distributed filesystem now.
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11:50am–12:30pm Thursday, March 28, 2019
Juan Paulo Gutierrez (Rakuten)
Average rating:
(4.75, 4 ratings)
Juan Paulo Gutierrez explains how a small team in Tokyo went through several evolutions as they built an analytics service to help 200+ businesses accelerate their decision-making process. Join in to hear about the background, challenges, architecture, success stories, and best practices as they built and productionalized Rakuten Analytics.
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11:50am–12:30pm Thursday, March 28, 2019
Francesco Mucio (Francescomuc.io)
Average rating:
(4.00, 2 ratings)
Francesco Mucio tells the story of how Zalando went from an old-school BI company to an AI-driven company built on a solid data platform. Along the way, he shares what Zalando learned in the process and the challenges that still lie ahead.
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2:40pm–3:20pm Thursday, March 28, 2019
Kapil Gupta (Airbnb)
Average rating:
(3.50, 4 ratings)
Kapil Gupta explains how Airbnb approaches the personalization of travelers’ booking experiences using machine learning.
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4:40pm–5:20pm Thursday, March 28, 2019
Christopher Lennan (idealo.de)
Average rating:
(4.00, 1 rating)
Idealo.de recently trained convolutional neural networks (CNN) for aesthetic and technical image quality predictions. Christopher Lennan shares the training approach, along with some practical insights, and sheds light on what the trained models actually learned by visualizing the convolutional filter weights and output nodes of the trained models.
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