Speaker: Anisha Nainani

Software Engineer @Paypal

Anisha has 2 plus years of experience in building big data platforms. Anisha did her Masters in Computer Science from University of Texas at Dallas in 2014 with a focus on Data Engineering. After that she joined Paypal as a Software Engineer, where she helped build a compute framework to provide unified experience to users to run SQL queries on any compute engine such as spark, presto, hive. Anisha is also the core contributor of PayPal Gimel’s HBASE & Aerospike APIs - enabling SQL & unified API for users to access HBASE & Aerospike at scale. Currently, she is working on PayPal’s Core Data Highway, which enables streaming data from Oracle to Kafka and other offline data stores in near real time.

Find Anisha Nainani 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.