Deploying a Model for Inference at Production Scale
This NVIDIA DLI course teaches teams how to deploy machine learning models on a GPU server using NVIDIA Triton Inference Server. It is especially useful for organizations that have moved beyond experimentation and need practical serving capability.
Delivery
Virtual, On-site, or Hybrid
Duration
4 hours
Product
NVIDIA Triton Inference Server
Role
ML Engineer
NVIDIA
Inference at ScaleProduction serving on GPU infrastructure
NVIDIA Triton
Best Fit
Audience Profile
Who This Program Is For
Built for practitioners who already train models and now need practical deployment and inference capability on GPU-based serving infrastructure.
Overview
Program Summary
Official NVIDIA DLI program focused on deploying machine learning models to GPU servers with NVIDIA Triton Inference Server.
Course Outline
Complete Module Sequence
Review the full module sequence for this program, including the primary topic coverage in each module where available.
1Module 1
Build the foundation for production inference
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Module 1
Build the foundation for production inference
Understand the core deployment patterns and operational considerations involved in moving trained models into production inference environments.
- Inference deployment foundations
- GPU-backed deployment workflows
2Module 2
Serve and manage models with Triton
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Module 2
Serve and manage models with Triton
Use NVIDIA Triton to expose models for inference while improving deployment readiness and scalability for AI applications.
- Serving models with Triton
- Production inference considerations
Coverage Areas
Topic Coverage
Coverage Item 1
Inference deployment foundations
Coverage Item 2
Serving models with Triton
Coverage Item 3
GPU-backed deployment workflows
Coverage Item 4
Production inference considerations
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.
- •Align the workshop to your primary model framework
- •Add serving architecture and observability guidance
- •Extend into performance optimization and enterprise rollout planning
