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. Nan Zhu and Felix Cheung dive into the internals of distributed training in XGBoost and demonstrate how XGBoost resolves the business problem in Uber with a scale to thousands of workers and tens of TB of training data. Read more.
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2:05pm2:45pm Wednesday, September 25, 2019
Location: 1A 21/22
Atul Gupte (Uber), Nikhil Joshi (Uber)
Uber is changing the way people think about transportation. As an integral part of the logistical fabric in 65+ countries around the world, it uses ML and advanced data science to power every aspect of the Uber experience—from dispatch to customer support. Atul Gupte and Nikhil Joshi explore how Uber enables teams 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 include distributed tracing systems like Zipkin and Haystack. Keshav Peswani and Ashish Aggarwal explore how combining them with real-time, intelligent alerting mechanisms helps in the automated detection of problems. Read more.
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2:55pm3:35pm Wednesday, September 25, 2019
Location: 1E 12/13
Tim McKenzie (Pitney Bowes)
Tim McKenzie examines why planning 5G network rollout and associated services requires a good understanding of location-based data. Accurate addressing and linking consumers to property or points of interest allows data enrichment with attributes, demographics and social data. Companies use location to organize and analyze network and customer data 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)
Uber takes data driven to the next level. It needs a robust system for discovering and managing various entities, from datasets to services to pipelines, and their relevant metadata isn't just nice—it's absolutely integral to making data useful. Kaan Onuk, Luyao Li, and Atul Gupte explore the current state of metadata management, end-to-end data flow solutions at Uber, and what’s coming next. Read more.
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5:25pm6:05pm Wednesday, September 25, 2019
Location: 1A 01/02
Brandy Freitas (Pitney Bowes)
Brandy Freitas examines the interplay between graph analytics and machine learning, improved feature engineering with graph native algorithms, and how to harness the power of graph structure for machine learning through node embedding. Read more.
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11:20am12:00pm Thursday, September 26, 2019
Location: 3B - Expo Hall
Brian Keng (Rubikloud)
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 they're robust and consistent with business goals. Brian Keng takes a deep dive into 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), Bo Yang (Uber)
Omkar Joshi and Bo Yang offer an overview of how Uber’s ingestion (Marmary) and observability team improved performance of Apache Spark applications running on thousands of cluster machines and across hundreds of thousands+ of applications and how the team methodically tackled these issues. They 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
Building a reliable big data platform is extremely challenging when it has to store and serve hundreds of petabytes of data in real time. Reza Shiftehfar 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 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 customers were using voice or email to contact agents for postsale 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. Madhu Gopinathan and Sanjay Mohan explain the high-level architecture and the business impact Myra created. 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)
Jonathan Tudor and Ross Schalmo explore how GE Aviation made it a mission to implement self-service data. To ensure success beyond initial implementation of tools, the data engineering and analytics teams created initiatives 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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