Whether you work at a software company that needs to improve customer retention, a financial services company that needs to mitigate risk, or a retail company interested in predicting customer purchasing behavior, your organization is tasked with preparing, managing, and gleaning insights from large volumes of data without wasting critical resources. Traditional CPU-driven data science workflows can be cumbersome, but with the power of GPUs, your teams can make sense of data quickly to drive business decisions.

In this workshop, you’ll gain an overview on how to build and execute end-to-end GPU-accelerated data science workflows. We will begin with RAPIDS™-accelerated data science libraries, followed by an intro to accelerating ETLs with Spark 3.0. Subsequently we will delve into designing Recommender Systems using Merlin. Finally you will see how to stitch all these libraries together intro a production system by leveraging NVIDIA Triton – our end-to-end MLOps platform and TensorRT to optimize model artifacts.

• An overview of the Accelerated Analytics technology stack from NVIDIA

• An ability to swiftly and easily instantiate development environments

• An introduction to optimizing your Deep-Learning models via TensorRT

• Productionise your ML models via NVIDIA Triton

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