Organizations across industries are leveraging computer vision (CV) to gather insights, improve the customer experience, and drive operational efficiencies. However, building a CV application requires large amounts of labeled data, software and hardware infrastructure to train the AI models, and tools to run real-time inference that will scale with demand. 

Join this webinar to find out how performance-optimized AI software and pre-trained models, available from the NVIDIA NGC™ catalog, help companies quickly build AI-powered applications with a fraction of the training data.

You’ll see how to deploy the software and the models from the NGC catalog through Jupyter Notebooks with a single click on Google Cloud Vertex AI, build an action recognition application service using various artifacts from the NGC catalog, and use this example as a template for building your own CV applications.

• How to deploy Jupyter Notebooks for AI software and ML models from the NGC catalog on Google Cloud Vertex AI

• How Google Cloud's Vertex AI Workbench helps users build ML models faster through access to data and analytics services, on-demand GPUs, distributed spark clusters, and pre-built runtimes for major frameworks

• How various CV models, including action recognition and object detection, help accelerate development

• How to customize models for your use case with NVIDIA TAO and run inference with DeepStream on scalable systems

Content

content goes here