“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Track: NVIDIA DLI Certificate of Competency
NVIDIAIntermediate
Introduction to Graph Neural Networks
This NVIDIA DLI course introduces the basic concepts, models, and applications of graph neural networks. It is a strong entry point for teams exploring graph-based representation learning and relational AI use cases.
Duration
8 hours
Level
Intermediate
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
Enterprise Track
NVIDIA
Graph Neural NetworksGR
Relationship-aware deep learning
NVIDIA Graph AI
GNN
On this page
Ideal for
Deep Learning PractitionerGraph Neural NetworksTailored Team DeliveryImplementation-Focused
How VNode delivers this
Who this is for, and how we run it
VNode ITeS delivers Introduction to Graph Neural Networks as an MCT-led NVIDIA program for Deep Learning Practitioner. Typical duration is 8 hours (Virtual, On-site, or Hybrid). Labs follow production-shaped scenarios rather than slide-only walkthroughs. Private cohorts can shift emphasis by role mix, workspace or repo constraints, and rollout timing.
Audience Profile
Built for these roles
Built for practitioners who already understand core deep learning and want to extend into graph-based modeling and relational AI techniques.
Overview
Executive overview
Official NVIDIA DLI course introducing the concepts, models, and applications of graph neural networks.
Readiness
Prerequisites
- Relevant foundational experience in the target technology area.
- Comfort with hands-on labs in a cloud or GPU-accelerated environment.
Program Outcomes
Capabilities your teams will gain
Strengthen capability in graph neural networks scenarios
Strengthen capability in nvidia deep learning scenarios
Strengthen capability in deep learning scenarios
Curriculum
Curriculum roadmap
1
Graph Neural Networks
2
NVIDIA Deep Learning
3
Deep Learning
1Module 1
Graph Neural Networks
+
Module 1
Graph Neural Networks
Cover graph neural networks skills and implementation practices aligned to Introduction to Graph Neural Networks.
- Cover graph neural networks skills
- implementation practices aligned to Introduction to Graph Neural Networks
2Module 2
NVIDIA Deep Learning
+
Module 2
NVIDIA Deep Learning
Cover nvidia deep learning skills and implementation practices aligned to Introduction to Graph Neural Networks.
- Cover nvidia deep learning skills
- implementation practices aligned to Introduction to Graph Neural Networks
3Module 3
Deep Learning
+
Module 3
Deep Learning
Cover deep learning skills and implementation practices aligned to Introduction to Graph Neural Networks.
- Cover deep learning skills
- implementation practices aligned to Introduction to Graph Neural Networks
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Implementation-focused labsPrivate cohort deliveryIntermediate practitioner depthBusiness outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Builds capability in graph neural network concepts that are increasingly relevant for fraud, recommendation, knowledge graph, and relationship-based AI scenarios.
- •Use your relationship-heavy use case as the framing scenario
- •Add fraud, recommendation, or knowledge graph examples
- •Extend into advanced graph ML implementation planning
Credentials
Certification & official source
- •NVIDIA DLI Certificate of Competency
Aligned to the official source referenced for this program.
View Official SourceResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Retail & E-commerce
Representative Retail Analytics Team
Instead of treating reporting as a tooling issue alone, the work focused on consistency, governance, and shared delivery practices across analysts and engineering teams.
- Higher consistency in report design practices
- Improved collaboration between analysts and engineering teams
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios