DevOps and AI on AWS: AIOps Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This course provides a comprehensive introduction to AIOps on AWS, combining foundational AI concepts with practical DevOps practices in the cloud. Designed for beginners with basic cloud knowledge, it guides learners through core AI and automation techniques used in modern IT operations. The course spans approximately 18-22 hours of content, delivered through lectures, hands-on labs, and guided projects, culminating in a final project that demonstrates real-world AIOps implementation.
Module 1: Foundations of Computing & Algorithms
Estimated time: 4 hours
- Discussion of best practices and industry standards in computing
- Introduction to computational thinking for problem solving
- Algorithm design and evaluation techniques
- Interactive lab: Building practical cloud-based solutions
- Guided project work with instructor feedback
Module 2: Neural Networks & Deep Learning
Estimated time: 3 hours
- Introduction to neural networks and deep learning fundamentals
- Hands-on exercises applying neural network techniques
- Evaluating model performance using metrics and benchmarks
- Discussion of best practices in model training and optimization
- Guided project work with instructor feedback
Module 3: AI System Design & Architecture
Estimated time: 2 hours
- Introduction to key concepts in AI system design
- Review of tools and frameworks used in AI architecture
- Design patterns for scalable and reliable AI systems
- Hands-on exercises in AI architecture implementation
- Assessment through quiz and peer-reviewed assignment
Module 4: Natural Language Processing
Estimated time: 3 hours
- Introduction to core NLP concepts and applications
- Understanding transformer architectures and attention mechanisms
- Implementing prompt engineering for large language models
- Interactive lab: Building practical NLP solutions
- Hands-on exercises in NLP techniques
Module 5: Computer Vision & Pattern Recognition
Estimated time: 2 hours
- Introduction to computer vision fundamentals
- Pattern recognition techniques and use cases
- Review of tools and frameworks in computer vision
- Case study analysis with real-world examples
- Assessment through quiz and peer-reviewed assignment
Module 6: Deployment & Production Systems
Estimated time: 4 hours
- Review of tools and frameworks for AI deployment
- Building and deploying AI-powered applications
- Interactive lab: Implementing production-ready systems
- Discussion of best practices in monitoring and automation
- Guided project work with instructor feedback
Prerequisites
- Basic understanding of cloud computing concepts
- Familiarity with IT operations and system monitoring
- Introductory knowledge of AWS services recommended
What You'll Be Able to Do After
- Apply computational thinking to solve complex engineering problems
- Build and deploy AI-powered applications on AWS
- Implement prompt engineering techniques for large language models
- Design and evaluate AI systems for real-world use cases
- Automate and optimize cloud operations using AIOps practices