AI Infrastructure : Cloud GPU Syllabus

Overview: This course provides a comprehensive exploration of AI infrastructure with a focus on cloud-based GPU utilization for modern AI workloads. Designed for advanced learners, it covers foundational computing concepts, deep learning, system design, NLP, computer vision, and deployment in production environments. The curriculum blends theoretical knowledge with hands-on labs and real-world case studies, requiring approximately 16–20 hours to complete.

Module 1: Foundations of Computing & Algorithms

Estimated time: 3 hours

• Introduction to key concepts in foundations of computing & algorithms
• Interactive lab: Building practical solutions
• Review of tools and frameworks commonly used in practice
• Case study analysis with real-world examples

Module 2: Neural Networks & Deep Learning

Estimated time: 3 hours

• Introduction to key concepts in neural networks & deep learning
• Guided project work with instructor feedback
• Review of tools and frameworks commonly used in practice
• Case study analysis with real-world examples

Module 3: AI System Design & Architecture

Estimated time: 2 hours

• Hands-on exercises applying AI system design & architecture techniques
• Guided project work with instructor feedback
• Case study analysis with real-world examples

Module 4: Natural Language Processing

Estimated time: 2 hours

• Introduction to key concepts in natural language processing
• Discussion of best practices and industry standards
• Assessment: Quiz and peer-reviewed assignment

Module 5: Computer Vision & Pattern Recognition

Estimated time: 4 hours

• Introduction to key concepts in computer vision & pattern recognition
• Hands-on exercises applying computer vision & pattern recognition techniques
• Case study analysis with real-world examples
• Assessment: Quiz and peer-reviewed assignment

Module 6: Deployment & Production Systems

Estimated time: 4 hours

• Interactive lab: Building practical solutions
• Case study analysis with real-world examples
• Assessment: Quiz and peer-reviewed assignment

Prerequisites

• Strong understanding of cloud computing fundamentals
• Familiarity with AI and deep learning basics
• Experience with programming and machine learning frameworks

What You'll Be Able to Do After

• Design and deploy AI-powered applications for real-world use cases
• Scale algorithms efficiently with growing data and computational demands
• Apply computational thinking to solve complex engineering problems
• Implement intelligent systems using modern AI frameworks and libraries
• Evaluate model performance using appropriate metrics and benchmarks