Course Description:
This course explores the architecture, deployment models, and optimization strategies for enterprise-grade AI infrastructure. Learners will compare on-premise and cloud environments, design scalable AI systems, and implement secure, high-performance agentic AI setups.
Lecture 1: On-Premise vs Cloud Deployment
Understand the trade-offs between on-premise and cloud AI deployments. Learn how security, compliance, customization, and costs influence infrastructure choices.
Key Objectives:
- Security and compliance considerations
- Customization and control requirements
- Cost-benefit analysis
Lecture 2: AI Infrastructure Architecture
Design robust AI infrastructure by selecting the right hardware, software stack, and scaling strategies. Learn methods for performance tuning and resource optimization.
Key Objectives:
- Hardware and software stack design
- Scalability planning
- Performance optimization
Lecture 3: On-Premise Agentic AI Infrastructure
Implement agentic AI systems within private infrastructure. Explore local data center setups, private cloud strategies, and techniques for reducing latency.
Key Objectives:
- Local data center deployment
- Private cloud strategies
- Latency optimization
