Microsoft AI Foundry Program Track — Available for Enterprise Team Delivery

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Program Outline

AIAdvancedNVIDIA LLM ToolingGenerative AI / LLM

Efficient Large Language Model Customizations

Focuses on applied and efficient ways to customize LLMs for real use cases.

Delivery

Virtual, On-site, or Hybrid

Duration

8 hours

Product

NVIDIA LLM Tooling

Role

AI Engineer

Lab-Based DeliveryCustomizable for TeamsOfficial Source Linked
Enterprise Track

Best Fit

AI EngineerGenerative AI / LLMTailored Team DeliveryImplementation-Focused

Audience Profile

Who This Program Is For

Built for teams tailoring LLMs to business-specific needs.

Overview

Program Summary

Official NVIDIA DLI workshop on efficient large-language-model customization strategies.

Program Outcomes

After this program, participants will be able to...

Understand efficient LLM customization approaches

Apply customization ideas to enterprise GenAI use cases

Delivery Formats

VirtualOnsiteHybridBootcampWeekendExecutive Workshop

Enterprise Suitability

For enterprise teamsFor certification preparationFor implementation readinessFor role-based upskilling

Course Outline

Complete Module Sequence

Review the full module sequence for this program, including the primary topic coverage in each module where available.

1

Module 1

Customize LLMs efficiently

+

Learn pragmatic approaches for adapting large language models efficiently.

  • Customization strategies
  • Efficient tuning patterns

Coverage Areas

Topic Coverage

Coverage Item 1

Customization strategies

Coverage Item 2

Efficient tuning patterns

Customization

Adapt This Program for Your Team

We can adapt this program around your team structure, platform priorities, delivery goals, and the scenarios your people need to work through in practice.

  • Use your domain-adaptation scenario

Engagement Confidence

A direct, founder-led review before scope, delivery model, and commercial terms are proposed.

Response window

< 1 business day

Client coverage

India + global teams

Engagement format

Virtual, on-site, hybrid