AI Infrastructure Cloud Tpus Course Syllabus

Overview: This course provides a comprehensive exploration of AI infrastructure with a focus on Google Cloud TPUs, designed for intermediate learners pursuing advanced AI and cloud engineering roles. The curriculum spans foundational computing principles to deployment of AI systems at scale, incorporating hands-on labs, real-world case studies, and guided projects. With approximately 15–20 hours of total learning time, the course blends theoretical understanding with practical implementation, preparing learners to design, optimize, and deploy high-performance AI workloads using Cloud TPUs.

Module 1: Foundations of Computing & Algorithms

Estimated time: 4 hours

• Case study analysis with real-world examples
• Guided project work with instructor feedback
• Discussion of best practices and industry standards
• Computational thinking for complex engineering problems

Module 2: Neural Networks & Deep Learning

Estimated time: 2.5 hours

• Introduction to key concepts in neural networks & deep learning
• Review of tools and frameworks commonly used in practice
• Understanding model performance evaluation metrics
• Discussion of best practices and industry standards

Module 3: AI System Design & Architecture

Estimated time: 2 hours

• Case study analysis with real-world examples
• Guided project work with instructor feedback
• Designing scalable algorithms for growing data
• Discussion of best practices and industry standards

Module 4: Natural Language Processing

Estimated time: 3 hours

• Interactive lab: Building practical NLP solutions
• Applying prompt engineering techniques for large language models
• Understanding transformer architectures and attention mechanisms
• Review of NLP tools and frameworks
• Hands-on exercises in natural language processing

Module 5: Computer Vision & Pattern Recognition

Estimated time: 3.5 hours

• Interactive lab: Building practical computer vision solutions
• Case study analysis with real-world examples
• Discussion of best practices and industry standards
• Hands-on exercises applying pattern recognition techniques

Module 6: Deployment & Production Systems

Estimated time: 1.5 hours

• Introduction to key concepts in deployment & production systems
• Hands-on exercises applying deployment techniques
• Building and deploying AI-powered applications for real-world use
• Discussion of best practices and industry standards

Prerequisites

• Basic understanding of cloud computing platforms and services
• Familiarity with machine learning and deep learning fundamentals
• Intermediate programming experience, preferably in Python

What You'll Be Able to Do After

• Design and implement scalable AI systems using Cloud TPUs
• Evaluate model performance using appropriate benchmarks and metrics
• Apply computational thinking to solve complex AI engineering problems
• Deploy AI-powered applications into production environments
• Optimize large-scale AI workloads for performance and efficiency