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Operating Deep Learning Pipelines Anywhere Using Kubeflow - Jörg Schad & Gilbert Song, Mesosphere

Kubeflow makes it very easy for data scientist to build their own data science pipeline with Jupyter Notebooks, TensorFlow, TensorBoard and Model serving. In this talk we will walk through building a production grade data science pipeline using Kubeflow and open source data, streaming and CI/CD automation tools. Audience will learn about need for data preparation (which is frequently performed using Apache Spark or Apache Flink), data storage (using HDFS, Cassandra), automation via CI/CD (using Jenkins) and request streaming (using Apache Kafka). In this talk we look at building and operate a complete deep learning pipeline around Kubeflow for multiple tenants and topics such as: * Data Preparation/Cleansing (using Apache Spark) * Data and Model Storage * Model Serving * Distributed Training * Monitoring * Automation using CI/CD * Infrastructure Management across multiple tenants

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