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 and deep learning workloads. Designed for experienced engineers and technical professionals, it covers core concepts in computing, neural networks, AI system design, natural language processing, and computer vision, culminating in deployment strategies for production AI systems. The course blends theoretical knowledge with hands-on labs and real-world case studies, requiring approximately 15–20 hours to complete, including quizzes, peer-reviewed assignments, and project work.

Module 1: Foundations of Computing & Algorithms

Estimated time: 3 hours

• Review of core computing principles and algorithmic thinking
• Introduction to tools and frameworks used in AI infrastructure
• Applying computational thinking to engineering problems
• Designing scalable algorithms for data-intensive systems

Module 2: Neural Networks & Deep Learning

Estimated time: 3 hours

• Understanding core AI concepts including neural networks
• Exploring deep learning architectures and training processes
• Interactive lab: Building and tuning neural networks
• Assessment through quiz and peer-reviewed assignment

Module 3: AI System Design & Architecture

Estimated time: 2 hours

• Principles of scalable AI system design
• Review of frameworks and tools for AI infrastructure
• Discussion of industry best practices and standards

Module 4: Natural Language Processing

Estimated time: 4 hours

• Understanding transformer architectures and attention mechanisms
• Applying prompt engineering techniques to large language models
• Hands-on NLP exercises using cloud-based GPU environments
• Interactive lab: Building practical NLP solutions

Module 5: Computer Vision & Pattern Recognition

Estimated time: 2 hours

• Introduction to key computer vision concepts
• Applying pattern recognition techniques using deep learning
• Guided project with instructor feedback

Module 6: Deployment & Production Systems

Estimated time: 4 hours

• Deploying AI models in cloud production environments
• Case study analysis of real-world AI deployment challenges
• Interactive lab: Building and optimizing production-ready systems

Prerequisites

• Familiarity with cloud computing fundamentals
• Basic understanding of AI and machine learning concepts
• Experience with programming and DevOps practices

What You'll Be Able to Do After

• Evaluate model performance using appropriate metrics and benchmarks
• Design and implement scalable AI systems on cloud platforms
• Optimize GPU utilization for deep learning and generative AI workloads
• Apply prompt engineering and transformer-based techniques effectively
• Deploy and manage AI models in production-grade cloud environments