Mar 15–18, 2020

Schedule: Case Studies sessions

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11:00am11:40am Tuesday, March 17, 2020
Location: LL21B
George Chkadua (TBC Bank), Levan Borchkhadze (TBC Bank)
TBC Bank is in transition from product-centric to a client-centric, and obvious applications of analytics are developing personalized next-best product recommendation for clients. George Chkadua and Levan Borchkhadze explain why the bank decided to implement the ALS user-item matrix factorization method and demographic model. As as result, the pilot increased sales conversion rates by 70%. Read more.
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11:50am12:30pm Tuesday, March 17, 2020
Location: LL21B
Secondary topics:  Streaming and IoT
Mark Grover (Lyft), Dev Tagare (Lyft)
Mark Grover and Dev Tagare offer you a glimpse into the end-to-end data architecture Lyft uses to reduce data lag in its analytical systems from 24+ hours to less than 5 minutes. You'll learn the what and why of tech choices, monitoring, and best practices. They outline Lyft's use cases, especially in ML model performance and evaluation. Read more.
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11:50am12:30pm Tuesday, March 17, 2020
Location: LL21 C
Secondary topics:  Technology Ethics
Guillaume Saint-Jacques (LinkedIn), Meg Garlinghouse (LinkedIn)
Most companies want to ensure their products and algorithms are fair. Guillaume Saint-Jacques and Meg Garlinghouse share LinkedIn's A/B testing approach to fairness and describe new methods that detect whether an experiment introduces bias or inequality. You'll learn about a scalable implementation on Spark and discover examples of use cases and impact at LinkedIn. Read more.
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1:45pm2:25pm Tuesday, March 17, 2020
Location: LL21B
Joseph Sirosh (Compass)
Compass is changing real estate by leveraging its industry-leading software to build search and analytical tools that help real estate professionals find, market, and sell homes. Joseph Sirosh details how Compass leverages AWS services, including Amazon Elasticsearch Service, to deliver a complete, scalable home-search solution. Read more.
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1:45pm2:25pm Tuesday, March 17, 2020
Location: LL21 C
Secondary topics:  Streaming and IoT
Minal Mishra (Netflix)
Minal Mishra walks you through Netflix's video player release process, the challenges with deriving time series metrics from a firehose of events, and some of the oddities in running analysis on real-time metrics. Read more.
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2:35pm3:15pm Tuesday, March 17, 2020
Location: LL21B
Secondary topics:  Security and Privacy
Sathya Chandran (DataVisor)
Sathya Chandran shares key insights into current trends of account takeover fraud by analyzing 52 billion events generated by 1.1 billion users and developing a set of user mobility features to capture suspicious device and IP-switching patterns. You'll learn to incorporate mobility features into an anomaly detection solution to detect suspicious account activity in real time. Read more.
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2:35pm3:15pm Tuesday, March 17, 2020
Location: LL21 C
Ankit Jain (Uber AI), Piero Molino (Uber AI Labs)
Ankit Jain and Piero Molino detail how to generate better restaurant and dish recommendations in Uber Eats by learning entity embeddings using graph convolutional networks implemented in TensorFlow. Read more.
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4:15pm4:55pm Tuesday, March 17, 2020
Location: LL21B
Mario A. Vinasco (Credit Sesame)
Uber spends hundreds of millions of dollars in marketing and is constantly optimizing the allocation of these budgets. It deploys complex models using Python and PyTorch and borrows from machine learning (ML) to speed up solvers to optimize marketing investment. Mario Vinasco explains the framework of the marketing spend problem and how it was implemented. Read more.
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4:15pm4:55pm Tuesday, March 17, 2020
Location: LL21 C
Lior Gavish (Barracuda)
Lior Gavish breaks down a machine learning (ML)-based system that detects a highly evasive type of email-based fraud. The system combines innovative techniques for labeling and classifying highly unbalanced datasets with a distributed cloud application capable of processing high-volume communication in real time. Read more.
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5:05pm5:45pm Tuesday, March 17, 2020
Location: LL21B
Harrison Wang (LiveRamp)
A migration to a new environment is never easy. Harrison Wang walks you through how LiveRamp tackled migrating its large-scale production workflows from its private data center to the cloud while maintaining high uptime. You'll learn the high-level steps and decisions involved, lessons learned, and what to realistically expect out of a migration. Read more.
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5:05pm5:45pm Tuesday, March 17, 2020
Location: LL21 C
Karthik Ramasamy (Streamlio), Anand Madhavan (Narvar)
Narvar originally used a large collection of point technologies such as AWS Kinesis, Lambda, and Apache Kafka to satisfy its requirements for pub/sub messaging, message queuing, logging, and processing. Karthik Ramasamy and Anand Madhavan walk you through how Narvar moved away from using a slew of technologies and consolidating their use cases using Apache Pulsar. Read more.
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11:00am11:40am Wednesday, March 18, 2020
Location: LL21B
Secondary topics:  Data Management and Storage
Maulik Soneji (Gojek), Dinesh Kumar (Gojek)
Maulik Soneji and Dinesh Kumar explore Gojek's event-processing library to consume events from Kafka and push it to BigQuery. All of its services are event sourced, and Gojek has a high load of 21K messages per second for a few topics, and it has hundreds of topics. Read more.
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11:00am11:40am Wednesday, March 18, 2020
Location: LL21 C
Joy Rimchala (Intuit), Diane Chang (Intuit)
Explainable AI (XAI) has gained industry traction, given the importance of explaining ML-assisted decisions in human terms and detecting undesirable ML defects before systems are deployed. Joy Rimchala and Diane Chang delve into XAI techniques, advantages and drawbacks of black box versus glass box models, concept-based diagnostics, and real-world examples using design thinking principles. Read more.
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11:50am12:30pm Wednesday, March 18, 2020
Location: LL21B
Secondary topics:  Streaming and IoT
Jeff Chao (Netflix)
Netflix has experienced an unprecedented global increase in membership over the last several years. Production outages today have greater impact in less time than years before. Jeff Chao details the open-sourced Mantis, which allows Netflix to continue providing great experiences for its members, enabling it to get real-time, granular, cost-effective operational insights. Read more.
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11:50am12:30pm Wednesday, March 18, 2020
Location: LL21 C
Patryk Oleniuk (Virgin Hyperloop One), Sandhya Raghavan (Virgin Hyperloop One)
Patryk Oleniuk and Sandhya Raghava investigate how to use demand data to improve on the design of the fifth mode of transport—Hyperloop. They discuss the passenger demand prediction methods and the tech stack (Spark, koalas, Keras, MLflow) used to build a deep neural network (DNN)-based near-future demand prediction for simulation purposes. Read more.
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1:45pm2:25pm Wednesday, March 18, 2020
Location: LL21B
Secondary topics:  Data Management and Storage
Qorry Asfar (Pusdeham ), Muhammad Asfar (University of Airlangga)
With the disclosure of the Cambridge Analytica scandal, political practitioners have started to adopt big data technology to give them better understanding and management of data. Qorry Asfar and Muhammad Asfar provide a big data case study to develop political strategy and examine how technological adoption will shape a better political landscape. Read more.
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1:45pm2:25pm Wednesday, March 18, 2020
Location: LL21 C
Utkarsh B (Flipkart), Giridhar Yasa (Flipkart)
Utkarsh B. and Giridhar Yasa lead a deep dive into architectural patterns and the solutions Flipkart developed to ensure business continuity to millions of online customers, and how it leveraged technology to avert or mitigate risks from catastrophic failures. Solving for business continuity requires investments application, data management, and infrastructure. Read more.
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2:35pm3:15pm Wednesday, March 18, 2020
Location: LL21B
ravi krishnaswamy (Autodesk)
Today’s applications interact with data in a distributed and decentralized world. Using graphs at scale, you can infer communities and your interaction by tracking access to common data across users and applications. Ravi Krishnaswamy displays a real-world product example with millions of users that uses the combined powers of Spark and graph databases to gain insights into customer workflows. Read more.
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2:35pm3:15pm Wednesday, March 18, 2020
Location: LL21 C
Micah Wylde (Lyft)
Lyft processes millions of events per second in real time to compute prices, balance marketplace dynamics, and detect fraud, among many other use cases. Micah Wylde showcases how Lyft uses Kubernetes along with Flink, Beam, and Kafka to enable service engineers and data scientists to easily build real-time data applications. Read more.
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4:15pm4:55pm Wednesday, March 18, 2020
Location: LL21B
Kelly Zhiling Wan (LinkedIn), Jason Wang (LinkedIn), Lili Zhou (LinkedIn)
Good customer services accelerate customers' cohesion toward a product, which increases product engagement and revenue spending. It's traditional to use customer surveys to measure how customers feel about services and products, but Kelly Wan, Jason Wang, and Lili Zhou examine an innovative data product to measure customer happiness from LinkedIn. Read more.
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4:15pm4:55pm Wednesday, March 18, 2020
Location: LL21 C
Penghui Li (Zhaopin), Neng Lu (StreamNative)
Penghui Li and Neng Lu walk you through building an event streaming platform based on Apache Pulsar and simplifying a stream processing pipeline by Pulsar Functions, Pulsar Schema, and Pulsar SQL. Read more.

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