AI Infrastructurecloud Tpu Zh Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a comprehensive introduction to AI infrastructure with a focus on Google Cloud TPUs, designed for intermediate learners with prior knowledge in cloud computing and AI fundamentals. The curriculum spans approximately 15-18 hours across six modules, combining theoretical concepts, hands-on labs, and real-world case studies. Learners will explore neural networks, deep learning, transformer architectures, and deployment strategies for scalable AI systems. Through quizzes, peer-reviewed assignments, and guided projects, students will gain practical experience in optimizing large-scale machine learning workloads using high-performance computing environments. Ideal for engineers and developers aiming to specialize in AI infrastructure roles.
Module 1: Foundations of Computing & Algorithms
Estimated time: 4 hours
- Review of core computational thinking principles
- Best practices and industry standards in AI engineering
- Tools and frameworks commonly used in AI infrastructure
- Case study analysis with real-world examples
Module 2: Neural Networks & Deep Learning
Estimated time: 2 hours
- Fundamentals of neural networks and deep learning
- Review of frameworks used in deep learning practice
- Best practices for training models efficiently
- Guided project work with instructor feedback
Module 3: AI System Design & Architecture
Estimated time: 3 hours
- Principles of scalable AI system design
- Real-world case study analysis
- Interactive lab: Building practical AI solutions
- Review of tools and architectural frameworks
Module 4: Natural Language Processing
Estimated time: 3 hours
- Introduction to transformer architectures and attention mechanisms
- Hands-on NLP techniques using large language models
- Implementing prompt engineering strategies
- Review of NLP-specific tools and frameworks
Module 5: Computer Vision & Pattern Recognition
Estimated time: 2 hours
- Core concepts in computer vision and pattern recognition
- Best practices in model design and evaluation
- Interactive lab: Building computer vision solutions
- Guided project with instructor feedback
Module 6: Deployment & Production Systems
Estimated time: 4 hours
- Strategies for deploying AI models at scale
- Hands-on exercises with production systems
- Optimizing performance on cloud infrastructure
- Guided project work with instructor feedback
Prerequisites
- Familiarity with cloud computing fundamentals
- Basic understanding of machine learning and AI concepts
- Experience with programming and DevOps practices
What You'll Be Able to Do After
- Design and implement scalable AI systems using cloud TPUs
- Apply deep learning and transformer-based models to real-world problems
- Optimize AI workloads for performance and efficiency
- Deploy and manage AI applications in production environments
- Solve complex engineering challenges using computational thinking