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Overview for PayPal

Massive Scale Anomaly Detection Framework

PayPal analyzes billions of events every day in real-time across a wide range of services, devices and locations. In a collaboration between their Platform engineering team and data science teams, they have built a generic framework for developing robust and scalable anomaly detection streaming applications, focusing on flexibility to support different types of statistical and machine learning models. Inspired by the design of scikit-learn and Spark MLlib, the data team has designed a simple pipeline-based API on top of Spark Structured Streaming, that captures common patterns of the anomaly detection domain. 

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Big Data, Fast Data @ PayPal

PayPal has over 7PB of data in Apache Kafka, so they've got some good experience with building fast data products. In this presentation Sid Anand the Chief Data Engineer at PayPal covers their architecture, like how they use change data capture and why you should use Apache Avro for your data in Kafka.

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Merchant Churn Prediction Using SparkML at PayPal

In this discussion PayPal will presents the techniques used to retain merchants using some of the Machine Learning models using SparkML platform.

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Graph Representation Learning to Prevent Payment Collusion Fraud

PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, Venkatesh Ramanathan a data scientist at PayPal presents how advanced graph processing and machine learning algorithms are applied at PayPal for fraud prevention.

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Large Scale Graph Processing & Machine Learning Algorithms for Payment Fraud Prevention:

PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, Venkatesh Ramanathan a senior data scientist at PayPal   presents how advanced graph processing and machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention. He elaborates on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. He also explains how they employ sophisticated machine learning tools – open source and in-house developed.

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Spark Compute as a Service at Paypal

In this session Prabhu Kasinathan, a chief data engineer in Big Data Platform at Paypal will walk through the top challenges faced by PayPal administrators, developers and operations and describe how Paypal’s SCaaS platform overcomes them by leveraging open source tools and technologies, like Livy, Jupyter, SparkMagic, Zeppelin, SQL Tools, Kafka and Elastic. You’ll also hear about the improvements PayPal has added, which enable it to run greater than 10,000 Spark applications in production effectively.

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