GenAI for DevOps Practitioners

Instructors: Ritesh Vajariya +1 more

What you'll learn

  •   Identify the capabilities of GenAI for basic role-specific DevOps functions.
  •   Evaluate the results of GenAI for a real-world application in DevOps functions.
  •   Compare and contrast the risks, challenges, and ethical considerations of GenAI in the workplace for DevOps functions in general.
  • Skills you'll gain

  •   CI/CD
  •   DevOps
  •   Continuous Deployment
  •   Kubernetes
  •   Terraform
  •   Prompt Engineering
  •   Jenkins
  •   Continuous Delivery
  •   Software Documentation
  •   Infrastructure as Code (IaC)
  •   Generative AI
  •   Data Ethics
  • There is 1 module in this course

    Through a combination of discussions, video demos, and guided hands-on activities, learners will gain an understanding of how GenAI can enhance productivity for code generation, infrastructure as code (IaC), continuous integration/continuous deployment (CI/CD) pipeline optimization, and automated documentation. This course is designed for team leads, managers, and DevOps engineers aiming to enhance efficiency and innovation by integrating GenAI tools into their workflows. It's also ideal for aspiring DevOps practitioners who want to future-proof their skills and gain a competitive edge by mastering GenAI in DevOps. Participants should have a solid grasp of DevOps fundamentals like CI/CD, infrastructure as code, and automation. Familiarity with tools like Git, Jenkins, Docker, Kubernetes, and experience in Python or Shell scripting is essential. An open, curious mindset towards exploring GenAI will be crucial for success. By the end of the course, learners will discover the capabilities of GenAI in enhancing code generation, infrastructure as code, and CI/CD pipeline optimization. They will apply GenAI techniques to real-world DevOps tasks, gaining hands-on experience and evaluating its impact on productivity. Additionally, learners will consider the ethical implications of GenAI, developing strategies for responsible integration while maintaining human oversight and accountability in their DevOps practices.

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