Best Practices for Real-time Intelligent Video Analytics
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**Katja is a research engineer focusing on deep learning, computer vision and inference optimization**. Larger and more complex Vision AI networks enable better accuracy and precision since they are able to encode more information. This increase in size and complexity is, in turn, naturally associated with trained AI models having lower throughput and larger memory requirements. With that, real-time AI inference is becoming a new great challenge in intelligent video analytics. NVIDIA’s approach at solving this problem relies on two major components: first, tuning AI models for performance depending on the target deployment hardware platform, and, second, optimizing the use of available GPUs. From this talk, **you will learn how to leverage this approach to achieve real-time inference performance** by using software tools like DeepStream SDK, TensorRT and Triton Inference Server.
Transcript
Katja is a research engineer focusing on deep learning, computer vision and inference optimization.
Larger and more complex Vision AI networks enable better accuracy and precision since they are able to encode more information. This increase in size and complexity is, in turn, naturally associated with trained AI models having lower throughput and larger memory requirements. With that, real-time AI inference is becoming a new great challenge in intelligent video analytics.
NVIDIA’s approach at solving this problem relies on two major components: first, tuning AI models for performance depending on the target deployment hardware platform, and, second, optimizing the use of available GPUs.
From this talk, you will learn how to leverage this approach to achieve real-time inference performance by using software tools like DeepStream SDK, TensorRT and Triton Inference Server.
About the speakers
Data scientist for computer vision, video analytics and deep learning at NVIDIA