Sep 23–26, 2019

Schedule: Transportation and Logistics sessions

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2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 08/10
Nan Zhu (Uber), Felix Cheung (Uber)
XGBoost has been widely deployed in companies across the industry. This talk begins with introducing the internals of distributed training in XGBoost and then demonstrate how XGBoost resolves the business problem in Uber with a scale to thousands of workers and 10s of TB training data. Read more.
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2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 21/22
Atul Gupte (Uber Technologies Inc.), Nikhil Joshi (Uber)
At Uber, we’re changing the way people think about transportation. As an integral part of the logistical fabric in 65+ countries around the world, we’re using ML and advanced data science to power every aspect of the Uber experience - from dispatch to customer support. In this talk, we’ll explore how we enable teams at Uber to transform insights into intelligence and facilitate critical workflows. Read more.
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2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 06/07
Keshav Peswani (Expedia Group), Ashish Aggarwal (Expedia Group)
Observability is the key in modern architecture to quickly detect and repair problems in microservices. Modern observability platforms have evolved beyond simple application logs and now include distributed tracing systems like Zipkin, Haystack. Combining them with real time intelligent alerting mechanisms with accurate alerts helps in automated detection of these problems. Read more.
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2:55pm3:35pm Wednesday, September 25, 2019
Location: 1E 12/13
Tim McKenzie (Pitney Bowes)
Planning 5G network rollout and associated services requires a good understanding of location based data. Accurate addressing and linking consumers to property parcels or points of interest allows data enrichment with property attributes, demographics and social data. Companies use location to organize and analyze network and customer data in order to understand where to target new services. Read more.
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2:55pm3:35pm Wednesday, September 25, 2019
Location: 1A 23/24
Kaan Onuk (Uber), Luyao Li (Uber), Atul Gupte (Uber)
At Uber’s scale and pace of growth, a robust system for discovering and managing various entities, from datasets to services to pipelines, and their relevant metadata is not just nice to have: it is absolutely integral to making data useful at Uber. In this talk, we will explore the current state of metadata management and end-to-end data flow solutions at Uber and what’s coming next. Read more.
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11:20am12:00pm Thursday, September 26, 2019
Location: 3B - Expo Hall
Brian Keng (Rubikloud Technologies Inc)
Automating decisions require a system to consider more than just a data-driven prediction. Real-world decisions require additional constraints and fuzzy objectives to ensure that they are robust and consistent with business goals. This talk will describe how to leverage modern machine learning methods and traditional mathematical optimization techniques for decision automation. Read more.
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1:15pm1:55pm Thursday, September 26, 2019
Location: 1A 23/24
Omkar Joshi (Uber Technologies), Bo Yang (uber inc)
Omkar Joshi and Bo Yang offer an overview of how Uber’s ingestion (Marmary) & observability team improved performance of Apache Spark applications running on thousands of cluster machines and across 100 thousands+ of applications and how they methodically tackled these issues. They will also cover how they used Uber’s open sourced jvm-profiler for debugging issues at scale. Read more.
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2:05pm2:45pm Thursday, September 26, 2019
Location: 1A 23/24
Reza Shiftehfar (Uber Technologies)
Building a reliable Big Data platform is extremely challenging when it has to store and serve 100s of PetaBytes of data in a real-time fashion . This talk reflects on the challenges faced and proposes architectural solutions to scale a Big Data Platform to ingest, store, and serve 100+ PB of data with minute level latency while efficiently utilizing the hardware and meeting the security needs. Read more.
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3:45pm4:25pm Thursday, September 26, 2019
Location: 1E 12/13
Madhu Gopinathan (MakeMyTrip), Sanjay Mohan (MakeMyTrip)
At MakeMyTrip, India’s leading online travel platform, customers were using voice or email to contact agents for post sale support. In order to improve the efficiency of agents and improve customer experience, MakeMyTrip developed a chatbot Myra using some of the latest advances in deep learning. In this talk, we will discuss the high level architecture and the business impact created by Myra. Read more.
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3:45pm4:25pm Thursday, September 26, 2019
Location: 1E 10/11
Jonathan Tudor (GE Aviation), Ross Schalmo (GE Aviation)
GE Aviation has made it a mission to implement Self-Service Data. To ensure success beyond initial implementation of tools, the Data Engineering and Analytics teams at GE Aviation created initiatives designed to foster engagement from an ongoing partnership with each part of the business to the gamification of tagging data in a data catalog to forming a Published Dataset Council. Read more.
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4:35pm5:15pm Thursday, September 26, 2019
Location: 1A 12/14
Brandy Freitas (Pitney Bowes)
In this session, Brandy Freitas from Pitney Bowes will cover the interplay between graph analytics and machine learning, improved feature engineering with graph native algorithms, and harnessing the power of graph structure for machine learning through node embedding. Read more.

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