OnDemand
Yyou’ll learn how to run and accelerate traditional x86 workloads on NVIDIA’s ARM-based CPU and CPU-GPU platforms: NVIDIA Grace and Grace Hopper.
In the CPU masterclass, join us online or on-site to learn:
Enroll in the NVIDIA Partner Expert program to receive invitations to all sessions and work towards earning your certificate. NVIDIA Partner Expert Program.
OnDemand
We’ll focus on networking where you will learn how to dimension and configure your customers’ networks for the best performing AI.
In the networking masterclass, join us online or on-site to learn:
Enroll in the NVIDIA Partner Expert program to receive invitations to all sessions and work towards earning your certificate: NVIDIA Partner Expert Program.
NOT IN USE
Thursday, February 29, 2024, 11:00 a.m. GMT
Conversational AI technologies are becoming ubiquitous, with countless products taking advantage of automatic speech recognition, natural language understanding, and speech synthesis coming to market. Thanks to new tools and technologies, developing conversational AI applications is easier than ever, enabling a much broader range of applications, such as virtual assistants, real-time transcription, and many more.
Oleg Sudakov is a deep learning solutions architect at NVIDIA, where he concentrates on Large Language Models training and Conversational AI.
Previously he worked as a machine learning engineer and data scientist in Apple, Huawei, and Rakuten. He has a strong background in natural language processing and speech processing.
Oleg is based in Germany.
Thursday, February 29, 2024, 14:00 p.m. GMT
In this session, we will do an in-depth presentation of Triton and its main components. We also will demonstrate how to quick-start and use Triton on real word applications on-prem, on the cloud, or in a mixed environment. In addition, we provide you with the scripts and code to jumpstart your Triton expertise.
TensorRT is an optimizing compiler of neural networks for inference. NVIDIA TensorRT-based applications perform up to 36X faster than CPU-only platforms during inference, enabling you to optimize neural network models trained on all major frameworks, calibrate for lower precision with high accuracy, and deploy to hyperscale data centers, embedded platforms, or automotive product platforms.
Sergio Perez is a Solution Architect at NVIDIA specialised in Conversational AI. He has experience in optimising training and inference of LLMs, building Retrieval Augmented Generation systems and working with companies in different sectors to leverage AI. His area of expertise at NVIDIA is quantisation and inference serving. Previously he worked as AI engineer at Graphcore and Amazon, and got a PhD in computational fluid dynamics from Imperial College London.
Thursday, February 29, 2024, 13:00 p.m. GMT
Data science empowers enterprises to analyze and optimize various aspects such as business processes, supply chains, and digital experiences. However, data preparation and machine learning tasks often consume a significant amount of time. In time-sensitive scenarios, like predicting demand for perishable goods, speeding up data science workflows becomes a crucial advantage. With RAPIDS, a collection of open-source GPU libraries, data scientists can enhance their Python toolchain, achieve higher productivity, and improve model accuracy with minimal code modifications.
Miguel Martínez is a senior deep learning data scientist at NVIDIA, where he concentrates on Large Language Models, Recommender Systems and Data Engineering Pipelines with RAPIDS.
Previously, he mentored students at Udacity's Artificial Intelligence Nanodegree. He has a strong background in financial services, mainly focused on payments and channels.
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Date & Time: Wednesday, April 22, 2018