AI Infrastructure: Cloud GPUs Syllabus

Overview: This course provides a comprehensive exploration of AI infrastructure with a focus on cloud-based GPU utilization for training and deploying deep learning models. Designed for intermediate learners, it covers foundational computing principles, neural networks, system design, natural language processing, computer vision, and deployment to production environments. The course includes hands-on labs, real-world case studies, and guided projects, requiring approximately 15–20 hours to complete. Ideal for engineers and developers aiming to specialize in scalable AI systems.

Module 1: Foundations of Computing & Algorithms

Estimated time: 4 hours

• Discussion of best practices and industry standards
• Interactive lab: Building practical solutions
• Hands-on exercises applying foundations of computing & algorithms techniques
• Core concepts in computing and algorithm design

Module 2: Neural Networks & Deep Learning

Estimated time: 2 hours

• Introduction to key concepts in neural networks & deep learning
• Hands-on exercises applying neural networks & deep learning techniques
• Interactive lab: Building practical solutions
• Understanding core AI concepts including neural networks and deep learning

Module 3: AI System Design & Architecture

Estimated time: 3 hours

• Introduction to key concepts in AI system design & architecture
• Interactive lab: Building practical solutions
• Case study analysis with real-world examples
• Guided project work with instructor feedback

Module 4: Natural Language Processing

Estimated time: 2 hours

• Hands-on exercises applying natural language processing techniques
• Case study analysis with real-world examples
• Review of tools and frameworks commonly used in practice
• Understanding transformer architectures and attention mechanisms

Module 5: Computer Vision & Pattern Recognition

Estimated time: 3 hours

• Review of tools and frameworks commonly used in practice
• Case study analysis with real-world examples
• Discussion of best practices and industry standards
• Techniques in image recognition and pattern detection

Module 6: Deployment & Production Systems

Estimated time: 4 hours

• Introduction to key concepts in deployment & production systems
• Interactive lab: Building practical solutions
• Guided project work with instructor feedback
• Review of tools and frameworks for scalable AI deployment
• Evaluating model performance using appropriate metrics and benchmarks

Prerequisites

• Basic understanding of cloud computing concepts
• Familiarity with AI and machine learning fundamentals
• Experience with programming and technical systems (e.g., Python, Linux, command line)

What You'll Be Able to Do After

• Build and deploy AI-powered applications for real-world use cases
• Design algorithms that scale efficiently with increasing data
• Implement intelligent systems using modern frameworks and libraries
• Evaluate model performance using appropriate metrics and benchmarks
• Understand and optimize GPU utilization in cloud environments for AI workloads