Speaker: Dheeraj Rampally

Senior Software Engineer @Paypal

Dheeraj has 5 plus years of experience in building high scalable big data applications. Initially he worked at Yahoo where he contributed to Supply forecasting, Ad-serving by building Data Pipelines in Apache Pig, Spark and Oozie. He also has hands on experience in Document based stores like Mongo DB and Columnar stores like Druid. Currently he is working as a senior software engineer at Paypal where he is the core contributor to the Data Catalog which constitutes building highly scalable low latency applications in Java and Data discovery in Scala. Additionally, he works on the Unified data api in Scala on Spark Compute. He is also a contributor of several core features in PayPal’s Gimel Elastic Search API.

Find Dheeraj Rampally at

2019 Tracks

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.

  • Handling Sequential Data Like an Expert / ML Applied to Operations

    Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.

    Half-day tracks