Microsoft Sessions at NVIDIA GTC Digital

This post has been republished via RSS; it originally appeared at: Azure Compute Blog articles.

Despite being converted into a digital-only event, this year’s GTC is certain not to disappoint. The big buzz around cloud-based GPUs is the introduction of deep learning and AI capabilities complimentary to existing visualization, rendering, and gaming workflows. Having on-demand versatility by which GPUs can be consumed and transacted empowers greater productivity and efficiency with enhanced VDI/DaaS, real-time visualizations, and more immersive gaming & entertainment experiences.

This year we are sharing examples of some of the most versatile GPU-powered resources anywhere in the public cloud, with a clear understanding of maximum cost-for-performance metrics. We’re focusing on three main application use cases for GPUs:

  • Visualization, including 3D design rendering, remote rendering, and desktop virtualization
  • AI for machine learning, model training and inferencing
  • Edge Computing for hybrid scenarios, decoupled environments, and IoT device ecosystems

Microsoft Digital Sessions at NVIDIA GTC

Microsoft will be supporting the following pre-recorded sessions at GTC Digital this year. Please note that all session content will be posted and available for consumption by Thursday March 26th, 2020.



Microsoft open sources breakthrough optimizations for large scale BERT Models

Emma Ning

Nathan Yan

Enabling Workloads Using High-End Graphics through Windows Virtual Desktop

Manvender Rawat

Denis Gundarev

Deploying your Models to GPU with ONNX Runtime for Inferencing in Cloud & Edge Endpoints

Manash Goswami

Kundana Palagiri

Speed Up Your Data Science Tasks by a Factor of 100+ Using AzureML & NVIDIA RAPIDS

Daniel Schneider

Cody Peterson

Tom Drabas

GPU Accelerated IoT Workloads at the Edge

Paul DeCarlo

Ian Davis

Operationalizing PyTorch Models Using ONNX and ONNX Runtime

Emma Ning

Spandan Tiwari

Accelerated Machine Learning at Scale with NVIDIA RAPIDS on Microsoft Azure

Tom Drabas

Using Metadata to Drive Your MLOps and Kubeflow Workloads

David Aronchick

AdaSum: Adaptive Summation of Gradients for Deep Learning 

Jaliya Ekanayake

Visual Anomaly Detection using NVIDIA DeepStream with Azure IoT

Paul DeCarlo

Emmanuel Bertrand

Ian Davis

DeepSpeed: System optimizations enable training deep learning models with over 100 billion parameters

Yuxiong He

Samyam Rajbhandari

Edge Computing for building Machine Learning pipelines using Azure Stack


Kirtana Venkatraman

Mahesh Yadav

Garvita Rai


NVIDIA DLI Training Powered by Azure

Microsoft is proud to host the NVIDIA DLI instructor-led online training covering AI, accelerated computing, and accelerated data science all powered on Microsoft Azure. 

This year’s GTC event is shaping up to mark a major leap forward in how GPUs are utilized for modern application & service development workflows.

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