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Wednesday, February 23, 2022 | 10:00am - 11:00am PT
AI inference can deliver faster, more accurate predictions to organizations of all sizes—but building a platform for production AI inference is hard.
Real-world use cases require different types of AI model architectures, and the models can contain hundreds of millions of parameters.
Models are trained in different frameworks (TensorFlow, PyTorch, XGBoost, Python, and others) and have different formats.
Applications have different requirements (real-time low latency, high-throughput batch, or streaming inputs), and then there are different execution environments (CPUs, GPUs, in the cloud, on premises, at the edge).
High-performance inference on specific hardware or in-framework is challenging because of competing constraints like latency, accuracy, throughput, and memory size that modern AI applications demand.
Join our webinar to explore how NVIDIA’s inference solution, including
open-source NVIDIA Triton™ Inference Server and NVIDIA® TensorRT™, delivers fast and scalable AI inference in production.
- How to optimize, deploy, and scale AI models in production using Triton Inference Server and TensorRT
- CPUs and GPUs, and with a model analyzer for efficient deployment
- How to standardize workflows to optimize models using TensorRT and framework Integrations with PyTorch and TensorFlow
- About real-world use cases of customers and the benefits they’re seeing with Triton and TensorRT
Join us after the presentation for a live Q&A session.
- New Jetson features like the Python GPIO library
- The anatomy of a typical AI project using Jetson Nano
- How to train a neural network for a custom task, like avoiding collisions with JetBot
- How to easily perform real-time object detection with Jetson Nano
- How to move beyond JetBot and create your own project
Join us after the presentation for a live Q&A session.
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Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
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Speakers
Senior Product Marketing Manager, NVIDIA
Shankar is Senior Product Marketing manager in the data center GPU team at NVIDIA. He is responsible for GPU software infrastructure marketing to help IT and DevOps easily adopt and seamlessly integrate GPUs in their infrastructure. Before NVIDIA, he held engineering, operations and marketing positions in both small and large technology companies. He holds business and engineering degrees.
Product Marketing Manager, NVIDIA
Jay Rodge is a product marketing manager for deep learning and inference products at NVIDIA driving launches and product marketing initiatives. Jay received his master’s degree in computer science from Illinois Tech, Chicago with a focus on computer vision and NLP. Before NVIDIA, Jay was an AI research intern at BMW Group solving problems using computer vision for BMW’s largest manufacturing plant.
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Webinar: Description here
Date & Time: Wednesday, April 22, 2018