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Starts 3 June 2025 22:51

Ends 3 June 2025

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DevOps and AI on AWS: Upgrading Apps with Generative AI

Explore how to enhance applications with generative AI on AWS, implementing customized text generation, prompt engineering, and advanced techniques while integrating DevOps practices for seamless development and deployment.
Amazon Web Services via edX

Amazon Web Services

500 Courses


2 weeks, 2-4 hours a week

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Free Online Course (Audit)

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Overview

Explore the intersection of DevOps and Generative AI on AWS in this hands-on course. Learn to enhance existing applications with powerful AI features using Amazon Bedrock's large language models (LLMs).

You'll gain practical experience in implementing customized text generation, mastering prompt engineering, and applying advanced techniques like fine-tuning and Retrieval Augmented Generation (RAG).

Syllabus

  • Introduction to DevOps and Generative AI
  • Overview of DevOps principles
    Introduction to Generative AI
    Importance of AI in modern applications
  • Understanding AWS for AI Applications
  • Overview of AWS services
    Introduction to Amazon Bedrock
    Setting up your AWS environment
  • Overview of Large Language Models (LLMs)
  • Basics of LLMs
    Applications and use cases
    Introduction to Amazon Bedrock's LLMs
  • Enhancing Applications with Generative AI
  • Use cases for AI enhancement
    Identifying opportunities for AI integration
    Planning your AI-driven application upgrade
  • Introduction to Prompt Engineering
  • Fundamentals of crafting effective prompts
    Techniques for improving prompt results
    Hands-on exercises with prompt engineering
  • Implementing Text Generation with LLMs
  • Basics of text generation
    Integrating LLM capabilities into applications
    Best practices for leveraging text generation
  • Advanced Techniques in AI Application Development
  • Fine-tuning models for specific tasks
    Exploring Retrieval Augmented Generation (RAG)
    Practical applications of fine-tuning and RAG
  • Hands-on Lab: Building AI Enhanced Features
  • Step-by-step guide to integrating AI features
    Customizing models for specific business needs
    Troubleshooting and optimizing AI systems
  • Case Studies and Real-world AI Implementations
  • Analyzing successful AI implementations
    Lessons learned from real-world examples
    Identifying common pitfalls and how to avoid them
  • Final Project: Upgrading an Application with AI
  • Project overview and requirements
    Designing an AI enhancement plan
    Implementing and presenting your upgraded application
  • Course Conclusion and Future of AI in DevOps
  • Summary of key concepts learned
    Discussion on the future trends in AI and DevOps
    Resources for continued learning and development

Taught by

Russell Sayers, Rafael Lopes and Morgan Willis


Subjects

Programming