“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
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 serving capability.
Duration
8 hours
Level
Intermediate
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
Enterprise Track
NVIDIA
Inference at ScaleINF
Production serving on GPU infrastructure
NVIDIA Triton
TRITON
On this page
Ideal for
Deep Learning PractitionerInferenceTailored Team DeliveryImplementation-Focused
How VNode delivers this
Who this is for, and how we run it
VNode ITeS delivers Deploying a Model for Inference at Production Scale 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 train models and now need deployment and inference capability on GPU-based serving infrastructure.
Overview
Executive overview
Official NVIDIA DLI program focused on deploying machine learning models to GPU servers with NVIDIA Triton Inference Server.
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 inference scenarios
Strengthen capability in nvidia triton / tensorrt scenarios
Strengthen capability in deep learning scenarios
Curriculum
Curriculum roadmap
1
Inference
2
NVIDIA Triton / TensorRT
3
Deep Learning
1Module 1
Inference
+
Module 1
Inference
Cover inference skills and implementation practices aligned to Deploying a Model for Inference at Production Scale.
- Cover inference skills
- implementation practices aligned to Deploying a Model for Inference at Production Scale
2Module 2
NVIDIA Triton / TensorRT
+
Module 2
NVIDIA Triton / TensorRT
Cover nvidia triton / tensorrt skills and implementation practices aligned to Deploying a Model for Inference at Production Scale.
- Cover nvidia triton / tensorrt skills
- implementation practices aligned to Deploying a Model for Inference at Production Scale
3Module 3
Deep Learning
+
Module 3
Deep Learning
Cover deep learning skills and implementation practices aligned to Deploying a Model for Inference at Production Scale.
- Cover deep learning skills
- implementation practices aligned to Deploying a Model for Inference at Production Scale
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: Supports production AI readiness by helping teams move beyond training into scalable model deployment and inference operations.
- •Align the workshop to your primary model framework
- •Add serving architecture and observability guidance
- •Extend into performance optimization and enterprise 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