“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Enterprise Program Brief
Rapid Application Development Using Large Language Models
This NVIDIA DLI course gives teams applied knowledge of LLM application development by exploring the open-source ecosystem, including pretrained LLMs and frameworks that speed up solution delivery. It is well suited for organizations moving quickly into applied generative AI development.
Program Snapshot
Engagement scoped to cohort
- MCT-led delivery
- Private cohort / virtual ILT
- Typical response: 1 business day
VNode ITeS delivers Rapid Application Development Using Large Language Models as an MCT-led NVIDIA program for AI / LLM Engineer. 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 developers who already understand core Python and basic deep learning concepts and now need a structured path into LLM application development.
- AI / LLM Engineer
- LLM Applications
- Tailored Team Delivery
- Implementation-Focused
Overview
Executive overview
Official NVIDIA DLI generative AI program focused on enterprise LLM application development using open-source models and frameworks.
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 llm applications scenarios
- Strengthen capability in nvidia nemo scenarios
- Strengthen capability in generative ai / llm scenarios
Enterprise Delivery
Official learning & delivery options
Review the vendor learning resource, then book a consultation or request a private cohort for your team.
Curriculum
Curriculum roadmap
- LLM Applications
- NVIDIA NeMo
- Generative AI / LLM
1Module 1
LLM Applications
+
Module 1
LLM Applications
Cover llm applications skills and implementation practices aligned to Rapid Application Development Using Large Language Models.
- Cover llm applications skills
- implementation practices aligned to Rapid Application Development Using Large Language Models
2Module 2
NVIDIA NeMo
+
Module 2
NVIDIA NeMo
Cover nvidia nemo skills and implementation practices aligned to Rapid Application Development Using Large Language Models.
- Cover nvidia nemo skills
- implementation practices aligned to Rapid Application Development Using Large Language Models
3Module 3
Generative AI / LLM
+
Module 3
Generative AI / LLM
Cover generative ai / llm skills and implementation practices aligned to Rapid Application Development Using Large Language Models.
- Cover generative ai / llm skills
- implementation practices aligned to Rapid Application Development Using Large Language Models
Delivery Models
Delivery models
Engagement Fit
Engagement fit
- Implementation-focused labs
- Private cohort delivery
- Intermediate practitioner depth
- Business outcome alignment
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Gives teams a fast path to enterprise LLM application development with widely used open-source tooling and NVIDIA-aligned implementation patterns.
- Use your internal assistant or knowledge workflow as the scenario
- Add retrieval, evaluation, or deployment emphasis
- Extend into secure enterprise implementation and rollout 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
