Can the program be customized?

Yes. Getting Started With AI on NVIDIA Jetson Nano supports customization around lab depth, demo context, certification focus, and team-specific scenarios.

Yes. This program includes instructor-led labs and practical exercises aligned to real implementation patterns.

How does this align with official certification or curriculum?

This program is aligned to the official Official Source curriculum and learning path referenced for this track.

How are prerequisites assessed?

We review prerequisite readiness during scoping and can recommend preparatory modules or a skills checkpoint before cohort start.

What happens after the program?

Private cohorts receive a handover summary, recommended next learning paths, and optional follow-on advisory for rollout or certification readiness.

Enterprise Program Brief

Getting Started With AI on NVIDIA Jetson Nano

Introduces AI project development on Jetson Nano through a applied deep learning classification workflow.

NVIDIABeginner to IntermediateEnterprise Track

Plan Private Delivery Consult a Solutions Architect

Download Program Brief (PDF)

Program Snapshot

Engagement scoped to cohort

Duration3 hours

FormatVirtual, On-site, or Hybrid

LevelBeginner to Intermediate

SubjectDeep Learning

MCT-led delivery
Private cohort / virtual ILT
Typical response: 1 business day

Scoping call typically scheduled within one business day

VNode ITeS delivers Getting Started With AI on NVIDIA Jetson Nano as an MCT-led NVIDIA program for Developer. Typical duration is 3 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 developers and teams beginning their embedded AI journey with NVIDIA Jetson.

Developer
Deep Learning
Tailored Team Delivery
Implementation-Focused

Overview

Executive overview

Official NVIDIA DLI course for building an AI classification project with computer vision models on Jetson Nano.

Prerequisites

Basic familiarity with Python helpful, but not required.

Program Outcomes

Capabilities your teams will gain

Understand the basics of Jetson-based AI development
Build a simple classification workflow on edge hardware

Enterprise Delivery

Official learning & delivery options

Review the vendor learning resource, then book a consultation or request a private cohort for your team.

View Official Learning Book a Consultation Request Private Cohort Open in learning platform Program brief (PDF)

Curriculum

Curriculum roadmap

Jetson AI basics
Classification workflow on edge hardware

Module 1

Start building AI on Jetson

Learn the implementation basics of using Jetson Nano for deep learning and computer vision projects.

Jetson AI basics
Classification workflow on edge hardware

Delivery Models

Delivery models

Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend

Engagement Fit

Engagement fit

Implementation-focused labs
Private cohort delivery
Beginner to Intermediate practitioner depth
Business outcome alignment

Enterprise Customization

Enterprise customization

Tailor this program to your organization's priorities: Provides a structured entry point into embedded AI and edge deployment on NVIDIA Jetson.

Use your edge AI use case
Add deployment and sensor integration follow-on topics

Credentials

Certification & official source

Aligned to the official source referenced for this program.

View Official Source

Resources

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.

Program brief (PDF)View Official Source

Enterprise Proof

Trusted delivery outcomes

“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”

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

Read engagement

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

Read engagement

Delivery Capability

Enterprise-grade instruction

View delivery capability profile

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

Plan delivery