Data analytics workflows have traditionally been slow and cumbersome, relying on CPU compute for data preparation, training, and deployment. Accelerated data science can dramatically boost the performance of end-to-end analytics workflows, speeding up value generation while reducing cost. Learn how companies like Spotify and Walmart use NVIDIA-accelerated data science.
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GPU-Accelerated Model Evaluation: How we took our offline evaluation process from hours to minutes with RAPIDS
Speakers: Joseph Cauteruccio, Machine Learning Engineer, Spotify; Marc Romeyn, Machine Learning Engineer, Spotify;
Learn how Spotify utilized cuDF and Dask-CUDF to build an interactive model evaluation system that drastically reduced the time it took to evaluate our recommender systems in an offline setting. As a result, model evaluations that previously took hours to complete as CPU workloads now run in minutes, allowing us to increase our overall iteration speed and thus build better models.
Accelerated ETL, Training and Inference of Recommender Systems on the GPU with Merlin, HugeCTR, NVTabular, and Triton
Speaker: Even Oldridge, Senior Manager, Recommender Systems Framework Team, NVIDIA
In this talk, we’ll share the Merlin framework, consisting of NVTabular for ETL, HugeCTR for training, and Triton for inference serving. Merlin accelerates recommender systems on GPU, speeding up common ETL tasks, training of models, and inference serving by ~10x over commonly used methods. Beyond providing better performance, these libraries are also designed to be easy to use and integrate with existing recommendation pipelines.
How Walmart improves computationally intensive business processes with NVIDIA GPU Computing
Speakers: Richard Ulrich, Senior Director, Walmart; John Bowman, Director, Data Science, Walmart
Over the last several years, Walmart has been developing and implementing a wide range of applications that require GPU computing to be computationally feasible at Walmart scale. We will present CPU vs. GPU performance comparisons on a number of real-world problems from different areas of the business and we highlight, not just the performance gains from GPU computing, but also what capabilities GPU computing has enabled that would simply not be possible on CPU-only architectures.
How Cloudera Data Platform uses a single pane of glass to deploy GPU accelerated applications s across hybrid and multi-clouds
Speakers: Karthikeyan Rajendran, Product Manager, NVIDIA; Scott McClellan, General Manager of Data Science, NVIDIA
Learn how Cloudera Data Platform uses a single pane of glass to deploy GPU-accelerated applications across hybrid and multi-clouds.
GPU-Accelerated, High-Performance Machine Learning Pipeline
Speaker: Lei Zhang, Senior Machine Learning Engineer, Adobe
The Adobe team is currently working with NVIDIA to build an unprecedented GPU-based, high-performance machine learning pipeline.
Click here to view all of the other Data Science sessions and demos on NVIDIA On-Demand.