Join the NGC team for a webinar and live Q&A on Aug. 25, at 10 a.m.
Overview
The article announces an upcoming webinar focused on building a computer vision service using NVIDIA NGC and Google Cloud. It highlights the ease of deploying AI solutions through containers from the NGC catalog via Google Cloud Marketplace to GKE.
What You'll Learn
1
How to use containers from the NGC catalog deployed from Google Cloud Marketplace to GKE
2
How to utilize the Transfer Learning Toolkit as a template for custom training data sets
3
How to deploy an NVIDIA Triton inferencing container from the GCP Marketplace that scales inference using GKE
Key Questions Answered
How can organizations improve product experience using computer vision?
Organizations can leverage computer vision to enhance product experience by utilizing AI solutions that improve production and drive operational efficiencies. This requires a robust infrastructure for training AI models and tools for real-time inference.
What is the benefit of using NGC containers with Google Cloud?
Using NGC containers allows organizations to deploy AI solutions with just one click from the Google Cloud Marketplace to GKE, simplifying the process of building, deploying, and running AI applications in a managed Kubernetes environment.
What is the Transfer Learning Toolkit used for?
The Transfer Learning Toolkit serves as a template for creating custom training data sets, enabling users to adapt pre-trained models to their specific needs, thereby accelerating AI workflows.
Technologies & Tools
Platform
Nvidia Ngc
Used for deploying AI containers.
Cloud Service
Google Cloud Marketplace
Provides access to NGC containers for deployment.
Kubernetes Service
Gke
Managed service for deploying and running AI solutions.
Inference Engine
Nvidia Triton
Used for scaling inference in AI applications.
Key Actionable Insights
1Utilize the NGC catalog to streamline your AI workflows by deploying pre-built containers directly from Google Cloud Marketplace.This approach saves time and resources, allowing teams to focus on developing and refining their AI models rather than managing infrastructure.
2Implement the Transfer Learning Toolkit to customize AI models with your own data, enhancing their performance for specific tasks.This is particularly useful for organizations that require tailored solutions but lack extensive datasets, as it allows for efficient adaptation of existing models.
3Leverage NVIDIA Triton for scalable inference by deploying it through GKE, ensuring your AI applications can handle varying loads effectively.This is crucial for applications that require real-time processing and need to scale based on user demand or data input.