Master NVIDIA GPUs, Omniverse, Digital Twins, AI Containers, Triton Inference, DeepStream, and ModelOps
What you will learn
Architect and deploy GPU-accelerated AI pipelines using NVIDIA hardware (A100, H100, L4, Jetson) and the full NVIDIA AI Enterprise software stack.
Optimize AI models for performance and efficiency using TensorRT, TAO Toolkit, and advanced quantization techniques for both cloud and edge deployments.
Implement real-time AI applications with DeepStream, RAPIDS, and Triton Inference Server for video analytics, sensor fusion, and data processing.
Integrate AI solutions with cloud, edge, and digital twin environments, leveraging Kubernetes, Helm, and Omniverse for scalable deployment and simulation.
Apply security, licensing, and containerization best practices to ensure enterprise-grade reliability and compliance in AI systems.
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- Gain a comprehensive understanding of the NVIDIA AI ecosystem, enabling you to leverage its full potential for sophisticated AI development.
- Develop proficiency in designing and implementing end-to-end AI solutions that are specifically optimized for NVIDIA’s cutting-edge GPU architecture.
- Acquire the skills to build resilient and scalable AI inference services, ensuring high throughput and low latency for demanding applications.
- Master the integration of AI models within dynamic and interactive digital twin environments, unlocking new possibilities for simulation and analysis.
- Learn to package, deploy, and manage AI workloads efficiently through containerization, ensuring portability and consistent performance across diverse environments.
- Explore the power of data science acceleration libraries to significantly speed up data preparation and feature engineering for AI model training.
- Understand the principles of MLOps specifically tailored for GPU-accelerated workflows, facilitating seamless model lifecycle management.
- Become adept at creating real-time AI processing pipelines for complex data streams, such as high-resolution video or sensor data.
- Explore the capabilities of NVIDIA’s platform for building intelligent edge devices and deploying AI models directly to the hardware.
- Learn strategies for optimizing AI model performance beyond standard techniques, focusing on hardware-specific optimizations.
- Understand the architectural considerations for deploying AI solutions in hybrid cloud and on-premises environments, ensuring flexibility and control.
- Develop the ability to troubleshoot and fine-tune GPU-accelerated AI applications for maximum efficiency and resource utilization.
- Acquire knowledge on managing AI infrastructure and resources effectively for large-scale deployments.
- Explore advanced use cases and industry applications of GPU-accelerated AI, broadening your practical understanding.
- Learn to implement robust security measures for AI systems deployed on NVIDIA hardware.
- PROS:
- Provides hands-on experience with industry-leading NVIDIA hardware and software.
- Equips learners with highly sought-after skills in the rapidly growing AI landscape.
- Offers a structured path to becoming a recognized expert in GPU-accelerated AI.
- Enables the development of sophisticated, performance-critical AI applications.
- CONS:
- Requires access to or simulation of powerful GPU hardware, which can be a cost barrier for some.
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