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

Talk : Gimel Codelab Speakers Office Hours

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • 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

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.