Unlocking AI Potential: Top NVIDIA AI and Deep Learning Sessions
Summary
NVIDIA’s GTC 21 event offers over 1,400 sessions covering the latest advancements in AI and deep learning technologies. This article highlights the top 5 technical sessions focusing on conversational AI, recommender systems, and AI workflows, providing insights into how these technologies can be leveraged for various applications.
Building Custom Conversational AI Apps
Tailoring Deep Learning Models to Enterprise Needs
Building a custom conversational AI app requires fine-tuning deep learning models to meet specific enterprise needs. This process involves several cycles of re-training, fine-tuning, and deploying the model until it satisfies the requirements. NVIDIA’s Transfer Learning Toolkit and Riva provide a comprehensive solution for customizing ASR and NLP pipelines to build production-ready conversational AI applications.
Accelerating Recommender Systems
Leveraging GPU Power with Merlin
Recommender systems are crucial for many applications, but they can be computationally intensive. NVIDIA’s Merlin framework accelerates recommender systems on GPUs, speeding up ETL tasks, training models, and inference serving by approximately 10 times compared to traditional methods. This is achieved through the use of NVTabular for ETL, HugeCTR for training, and Triton for inference serving.
Simplifying AI Workflows
The Power of NGC
NVIDIA’s NGC catalog offers a range of tools and SDKs to simplify AI development. By using NGC software, developers can build and deploy AI solutions faster across various platforms, including on-premises, hybrid, and edge environments. A session at GTC 21 demonstrates how to build a conversational AI solution using NGC artifacts, including a Jupyter notebook, to streamline the development process.
Enhancing Video Conferencing
NVIDIA Maxine
NVIDIA Maxine is an accelerated platform SDK for developers of video conferencing services. It reduces video bandwidth usage down to one-tenth of H.264 using AI video compression and offers features such as face alignment, gaze correction, face re-lighting, and real-time translation. This session at GTC 21 provides insights into the latest updates and innovations in NVIDIA Maxine.
Deploying AI Models at Scale
Triton Inference Server
Deploying AI models at scale in production can be challenging. NVIDIA’s Triton Inference Server simplifies this process by allowing teams to deploy trained AI models from any framework on any GPU- or CPU-based infrastructure. This session at GTC 21 covers high-performance inference serving with Triton’s concurrent execution, dynamic batching, and integrations with Kubernetes and other tools.
Table: Key Sessions at GTC 21
| Session | Description |
|---|---|
| Building and Deploying a Custom Conversational AI App | Customizing ASR and NLP pipelines with NVIDIA Transfer Learning Toolkit and Riva. |
| Accelerated ETL, Training and Inference of Recommender Systems | Accelerating recommender systems on GPUs with Merlin, HugeCTR, NVTabular, and Triton. |
| Accelerating AI Workflows with NGC | Building conversational AI solutions using NGC artifacts and Jupyter notebooks. |
| NVIDIA Maxine: An Accelerated Platform SDK | Enhancing video conferencing with AI video compression and real-time translation. |
| Easily Deploy AI Deep Learning Models at Scale with Triton Inference Server | Deploying AI models at scale with Triton’s concurrent execution and dynamic batching. |
Further Reading
- Deep Learning Demystified: Understanding the fundamentals of accelerated data analytics and deep learning.
- Fundamentals of Deep Learning for Multi-GPUs: Scaling deep learning training to multiple GPUs.
- Maximize AI Inference Serving Performance with NVIDIA Triton Inference Server: Optimizing AI model deployment with Triton.
- Accelerate PyTorch Inference with TensorRT: Enhancing PyTorch inference with NVIDIA TensorRT.
- A Zero-code Approach to Creating Production-ready AI Models: Using NVIDIA TAO for guided model adaptation.
Conclusion
NVIDIA’s GTC 21 offers a wealth of information on the latest AI and deep learning technologies. The top 5 technical sessions highlighted in this article provide valuable insights into conversational AI, recommender systems, and AI workflows, offering practical solutions for developers and enterprises looking to leverage these technologies for various applications.