NVIDIA Deep Learning Institute
Self-paced courses
Browse hands-on learning in deep learning, generative AI, data science, and accelerated computing.
Browse NVIDIA DLI coursesNVIDIA
NVIDIA AI & GPU Computing
Learn AI foundations and accelerated computing, then explore GPU-aware inference, NVIDIA NIM deployment, and production operations.
Start with the first guideYour outcome
Explain the path from model and hardware requirements to a tested, observable inference service.
Core curriculum
The skills to practice
Deep learning foundations
GPU and CUDA concepts
Model serving
NVIDIA NIM
Performance and capacity
Observability and deployment
Guided path
NVIDIA AI platform and inference engineer
Learn the model and GPU foundations, try NVIDIA's hands-on material, and progress toward a measured NIM-based inference deployment.
Suggested pace: 5 weeks · Capstone: A benchmarked NVIDIA NIM inference service
0 of 5 complete
- 01
Build AI and accelerated-computing foundations
Review deep-learning and GPU concepts; identify prerequisites before selecting a hands-on course.
- 02
Choose a structured DLI course
Complete a course aligned to your level and capture the exercises or certificate evidence it provides.
- 03
Check NIM prerequisites and fit
Match a model and deployment target against hardware, software, licensing, and access requirements.
- 04
Run an inference quickstart
Send a test request and record throughput, latency, resource use, and failure behavior.
- 05
Plan operations and scaling
Verify support constraints and define health checks, logs, capacity thresholds, and a safe update plan.
Provider learning materials
Learn from official training and documentation.
Migo Mind's sequence is an independent study map. These links open the provider's own courses and technical references; availability, account requirements, and pricing can change.
NVIDIA Deep Learning Institute
DLI training and certification
Compare self-paced learning, instructor-led workshops, and available certificates.
Explore DLI programsNVIDIA documentation
Get started with NVIDIA NIM
Review prerequisites and choose a deployment path for large language and vision-language model microservices.
Open the NIM getting-started guideNVIDIA documentation
Run a NIM inference quickstart
Learn the high-level steps for preparing an environment, starting a NIM container, and sending an inference request.
Follow the NIM quickstartNVIDIA documentation
NIM support and operations
Check supported models, validated hardware profiles, software prerequisites, observability, and deployment guides.
Review deployment requirements