AI and Data in Business
NVIDIA AI Enterprise and NIM Deployment
This in-depth course develops directly applicable capability in NVIDIA AI Enterprise and NIM Deployment. It connects NVIDIA AI Enterprise Architecture, GPU Infrastructure and Runtime Preparation, and NVIDIA NIM Microservices to the decisions, controls, and activities participants need to perform in their workplace.
Overview
Practical learning for workplace transfer.
This in-depth course develops directly applicable capability in NVIDIA AI Enterprise and NIM Deployment. It connects NVIDIA AI Enterprise Architecture, GPU Infrastructure and Runtime Preparation, and NVIDIA NIM Microservices to the decisions, controls, and activities participants need to perform in their workplace. The five-module curriculum progresses toward Configured Deployment Workshop, using evidence, scenarios, and work products appropriate to the subject.
Prerequisites
Relevant workplace familiarity is recommended; exact technical prerequisites can be adapted to the participant group.
Objectives
- Analyze nvidia ai enterprise architecture, including certified software stack, drivers, cuda libraries, and supported infrastructure.
- Configure or structure gpu infrastructure and runtime preparation, including gpu drivers, container toolkit, and runtime validation.
- Evaluate nvidia nim microservices, including nim container architecture, model profiles, and inference engines.
- Manage inference performance and operations, including concurrency, batching, latency, throughput, and gpu utilization.
- Apply configured deployment workshop, including prepare a controlled nim deployment specification.
Target audience
- Professionals responsible for this subject area
- Managers, supervisors, and team leaders
- Analysts, specialists, engineers, or coordinators working with the relevant processes
- Project, implementation, assurance, or improvement team members
- Professionals preparing for broader responsibilities in this field
Program outline
A clear structure for the learning journey.
Outline points are grouped in one designed block instead of being treated as separate module cards.
01NVIDIA AI Enterprise Architecture3 topics
- Certified software stack, drivers, CUDA libraries, and supported infrastructure
- NVIDIA AI Enterprise deployment patterns across data center and cloud
- Licensing, compatibility matrices, and release lifecycle decisions
02GPU Infrastructure and Runtime Preparation3 topics
- GPU drivers, container toolkit, and runtime validation
- MIG partitioning, resource isolation, and workload placement
- Health checks with DCGM and infrastructure telemetry
03NVIDIA NIM Microservices3 topics
- NIM container architecture, model profiles, and inference engines
- Registry access, image selection, and endpoint deployment
- Model cache, secrets, persistent storage, and network exposure
04Inference Performance and Operations3 topics
- Concurrency, batching, latency, throughput, and GPU utilization
- Autoscaling and resource requests for Kubernetes deployments
- Logs, metrics, failure diagnosis, and rollback procedures
05Configured Deployment Workshop3 topics
- Prepare a controlled NIM deployment specification
- Deploy and test an inference endpoint in a training environment
- Document security controls, monitoring thresholds, and operating handover
Materials provided
- ○ Course-specific presentation slides
- ○ Guided exercises, scenarios, or configured-environment activities appropriate to the subject
- ○ Course-specific worksheets, checklists, or calculation templates
- ○ Applied workplace case materials
- ○ 4D Certificate of Completion issued by 4D Training & Consultancy
- ○ Post-course support for implementation questions
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
4D Training & Consultancy adapts the program to the client’s operating environment. Delivery combines structured explanation with subject-specific analysis, exercises, and implementation decisions so participants can transfer the learning to real responsibilities without implying vendor authorization.
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