50x faster performance with Ray and NVIDIA GPU-accelerated compute

ON DEMAND | Originally Aired March 8, 2022

Watch On Demand

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.

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 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.