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    50x Faster Performance with Ray and NVIDIA GPU-Accelerated Compute

    ON DEMAND | Originally Aired March 8, 2022

    Harness NVIDIA GPU-Accelerated Parallelization with Ray

    Apache Spark has been the incumbent distributed compute framework for the past 10+ years. But the overhead and complexity of Spark has led the longtime leader to become eclipsed by new frameworks like Ray.

    In this technical talk, we will provide an introductory overview of Ray, its origin, strengths, weaknesses, and best practices for using it. You'll learn reasons why you should choose Ray based on available compute infrastructure, data volumes, workload complexity, and more.

    We'll show Ray in action, demonstrating performance gains from NVIDIA GPU-acceleration. 

    Watch On Demand

    What's in store for you

    The what and
    why of Ray

    Discover Ray's history and learn its intended use cases in data science work

    Getting started with
    Ray & MLOps

    Learn strategies to start taking advantage of Ray's benefits quickly and easily using MLOps

    See Ray in action
    for ML model tuning

    See a demo of an NVIDIA GPU-accelerated hyperparameter optimization workflow

    Meet the speaker

    Nikolay Manchev

    Nikolay Manchev is the Principal Data Scientist for EMEA at Domino Data Lab.

    In this role, Nikolay helps clients from a wide range of industries tackle challenging machine learning use-cases and successfully integrate predictive analytics in their domain-specific workflows. He holds an MSc in Software Technologies, an MSc in Data Science, and is currently undertaking postgraduate research at King's College London. His area of expertise is Machine Learning and Data Science, and his research interests are in neural networks and computational neurobiology.

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