Note - SPPU's Degreeplus Beta Version is up, stay tuned for more details.
Note - SPPU's Degreeplus Beta Version is up, stay tuned for more details.

Course

AI for DevOps

48 hours 01 minutes

Credits: Optional Leanring

Description

In this course, learners will develop a comprehensive understanding of DevOps principles and practices, enhanced by generative AI capabilities — from foundational culture and toolchain selection through to AI-powered automation, cloud deployment, and the future of intelligent DevOps. Learners will explore DevOps culture and mindset, the DevOps lifecycle, Agile and DevOps methodologies, and MLOps principles, before implementing CI/CD pipelines and continuous testing approaches. They will build a strong generative AI foundation covering GPT models, machine learning, deep learning, prompt engineering, and responsible AI use, then apply AI to IT integration, automation, and the development of AI-powered IT solutions. The course then examines how AI transforms every stage of the DevOps pipeline — including CI/CD, test automation, infrastructure orchestration, monitoring and observability, and release management — before concluding with DevOps cloud strategy and automation across AWS, Azure, and Google Cloud Platform.

What Students Will Learn

  • DevOps Mindset for Leadership
  • DevOps - More Than Just Dev & Ops
  • Building a DevOps Culture
  • Maturing DevOps Practice in the Enterprise
  • DevOps Lifecycle and Toolchain
  • DevOps Tools: Selecting the Right Tools

  • DevOps Agile Development: Agile Processes for DevOps
  • DevOps Agile Development: DevOps Methodologies for Developers
  • Introduction to DevOps Principles for Machine Learning

  • CI/CD Implementation for DevOps
  • DevOps Continuous Testing: Testing Approaches
  • DevOps Continuous Testing: Testing Methodologies

  • Generative AI, Prompting and Ethics Awareness (Beginner)
  • An Introduction to Generative AI
  • An Introduction to GPT Models
  • Artificial Intelligence and Machine Learning
  • Deep Learning and Neural Networks
  • Getting Started with Prompt Engineering
  • Exploring Prompt Engineering Techniques
  • Case Studies in Prompt Engineering
  • Considerations for Using AI Responsibly

  • Generative AI Foundations: IT Integration with Generative AI
  • Generative AI Foundations: Advanced Generative AI Techniques for IT
  • Generative AI Foundations: Ethical & Responsible Use of AI in IT
  • AI in IT Automation: Integrating AI Automation in IT Operations
  • AI in IT Automation: Developing AI-powered IT Solutions

  • Introduction to Using AI-powered DevOps
  • Using AI-powered Cloud Platforms for DevOps
  • AI Tools for DevOps CI/CD Pipelines
  • AI Test Automation for DevOps
  • AI Infrastructure Orchestration for DevOps
  • AI Monitoring & Observability for DevOps
  • AI Release Management for DevOps
  • Future of AI in DevOps

  • DevOps and Cloud Strategy
  • DevOps Cloud Automation: AWS DevOps Tools
  • DevOps Cloud Automation: Advanced AWS Pipelines and DevOps Using Azure
  • DevOps Cloud Automation: DevOps with Google Cloud Platform

  • Generative AI Security: Theories and Practices
  • Generative AI: Navigating the Course to the Artificial General Intelligence Future
  • AWS DevOps Engineer Professional Certification Guide
  • The Quick Guide to Prompt Engineering
  • DevOps Automation Cookbook
  • Investments Unlimited: A Novel About DevOps, Security, Audit Compliance, and Thriving in the Digital Age

Overall Learning Outcomes

  • Explain DevOps culture, mindset, and leadership principles and describe the DevOps lifecycle and toolchain
  • Select and apply appropriate DevOps tools and mature DevOps practices across enterprise environments
  • Apply Agile methodologies within a DevOps context, including MLOps fundamentals for machine learning workflows
  • Implement CI/CD pipelines and apply continuous testing approaches and methodologies
  • Explain generative AI, GPT models, machine learning, deep learning, and neural network concepts
  • Apply structured prompt engineering techniques to design effective and responsible AI interactions
  • Integrate generative AI into IT operations and develop AI-powered IT solutions using advanced techniques
  • Apply AI automation to IT operations including infrastructure, monitoring, and service management
  • Use AI to enhance CI/CD pipelines, test automation, infrastructure orchestration, and release management
  • Apply AI-powered monitoring and observability tools to maintain DevOps system health and performance
  • Deploy and manage DevOps workflows across AWS, Azure, and Google Cloud Platform
  • Evaluate the future trajectory of AI in DevOps and prepare for emerging AI-driven engineering practices