Duration: 5 days
Course Overview
Module 1: Introduction to AI & AI Evolution
1. Overview of AI & Industry Use Cases
- Definition of AI, ML, Deep Learning, and Generative AI
- AI applications in different industries (Healthcare, Finance, Manufacturing, etc.)
- The role of AI in modern enterprise operations
2. Evolution of AI
- AI history and major breakthroughs
- Transition from rule-based AI to machine learning
- Deep learning and its impact on AI models
3. Generative AI & Emerging Trends
- Introduction to Generative AI
- Use cases: Image generation, Chatbots, Music synthesis, Video creation
- Ethical considerations in AI-generated content
4. Role of GPUs in AI Computing
- Why GPUs are preferred for AI workloads
- CUDA architecture and Tensor Cores
- Hardware accelerators vs. CPUs for AI
5. AI Software Stack
- Overview of AI software stacks (TensorFlow, PyTorch, NVIDIA TensorRT)
- Importance of optimizing software and hardware together
- AI workloads in cloud and on-premises environments
6. Hands-on Lab
- Setting up an AI development environment with GPU support
- Running a basic deep learning model using TensorFlow/PyTorch
Module 2: AI Infrastructure & Compute Platforms
1. Hands-on Lab
- Introduction to NVIDIA DGX Systems and their role in AI training
- Cloud-based AI solutions (AWS, Azure, Google Cloud)
2. AI Storage & Data Management
- Types of AI storage solutions
- Data preprocessing and pipeline optimization
3. AI Networking & High-Speed Data Transfers
- Role of InfiniBand and RDMA in AI networking
- High-speed interconnects for distributed training
4. Energy-Efficient AI Computing
- Sustainable AI computing strategies
- Reducing carbon footprints in AI operations
5. Reference Architectures for AI Deployment
- Importance of Reference Architectures (RAs)
- Designing scalable AI solutions
6. Hands-on Lab
- Setting up AI infrastructure on cloud platforms
- Deploying AI models using Kubernetes and Docker
Module 3: AI Operations & Management
1. Hands-on Lab
- AI workload monitoring tools (NVIDIA Nsight, Prometheus, Grafana)
- Detecting and resolving AI performance bottlenecks
2. AI Cluster Orchestration
- Kubernetes for AI workload orchestration
- Slurm for AI job scheduling
3. AI Job Scheduling & Workload Management
- Optimizing AI jobs across multiple GPUs
- Dynamic resource allocation for AI workloads
4. Hands-on Lab
- Monitoring AI workloads using Prometheus and Grafana
- Deploying AI workloads using Kubernetes
Module 4: Transition to Cloud AI Solutions
1. On-Prem vs. Cloud AI Deployment
- Comparing on-prem AI infrastructure with cloud-based AI solutions
- Cost-benefit analysis of cloud AI services
2. Hybrid Cloud AI Architectures
- Strategies for combining on-prem and cloud AI environments
- NVIDIA AI Enterprise solutions for hybrid AI workloads
3. Hands-on Lab
- Deploying an AI model on AWS SageMaker
- Managing AI workloads using NVIDIA AI Enterprise
Module 5: Certification Preparation & Final Assessment
1. Certification Exam Topics Review
- Key concepts and best practices from the course
- Sample questions and discussion
2. Mock Exams & Practical Assignments
- Hands-on problem-solving exercises
- Full-length mock exam
3. Final Q&A and Certification Readiness
- Review and clarification of key topics
- Exam-taking strategies
