Speaker: Deepak Chandramouli

Analytics Tech Lead @PayPal

Deepak has over 13 years of experience in Data Engineering & 5 years of expertise building scalable data solutions in the Big Data space. He worked on building Apache Spark based Foundational Big Data Platform during the incubation of PayPal's Data lake. He has applied experience in implementing spark based solutions across several types of No-SQL, Key-Value, Messaging, Document based & relational systems. Deepak has been leading the initiative to enable access to any type of storage on Spark via - unified Data API, SQL, tools & services, thus simplifying analytics & large-scale computation-intensive applications.

Find Deepak Chandramouli at

Talk : Gimel: PayPal’s Analytics Data Platform

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.