DevOps Intelligence AI-Optimised Development
Credit points
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Online Real Time
VU Brisbane
VU Sydney
Prerequisites
Co-requisites
(Corequisite units must be studied concurrently with this unit)
Overview
In this unit, you will design intelligent DevOps workflows that integrate artificial intelligence to automate deployment, monitoring, and infrastructure management. You will explore AI-powered CI/CD pipelines, predictive analytics, cloud orchestration, and AI-enhanced security strategies to build scalable and self-healing systems. Case studies connect technical skills to industry-driven practices. By the end of the unit, you will be able to implement AI-driven DevOps solutions and evaluate their impact: preparing you for roles in intelligent automation, cloud engineering, DevSecOps, or advanced DevOps practice.
Learning Outcomes
On successful completion of this unit, students will be able to:
- Design and implement AI-powered DevOps pipelines for automated software deployment and operations management.
- Implement AI-driven monitoring and predictive analytics to optimise system performance and resource allocation.
- Experiment AI-enhanced security measures to detect, prevent, and mitigate security risks in DevOps workflows.
- Automate cloud infrastructure management using AI-based orchestration tools for scalability, fault tolerance, and self-healing capabilities individually and as a part of team.
- Critically analyse real-world case studies of AI-driven DevOps strategies and their impact on software development efficiency.
Assessment
For Melbourne campuses
Assessment type: Laboratory Work | Grade: 20%
Lab Practical Test on AI-Assisted Development Exercise
Assessment type: Assignment | Grade: 45%
Assignment on AI-Optimised DevOps design and Implementation (Group) with presentation, demonstration and Q&A verification.
Assessment type: Case Study | Grade: 35%
Case study on scenario based problems with verified live interaction.
Assignment:
Includes structured milestone reviews and in-class progress checks. Individual contributions are logged, validated, and justified through recorded peer review or demonstration to ensure authenticity.
Case Study:
Students apply concepts to unpredictable data science scenarios. Responses are evaluated based on reasoning, method selection, and outcome justification, with an in-class component or oral defence verifying understanding.
Required reading
Nil.
As part of a course
This unit is not compulsory for any specific course. Depending on the course you study, this unit may be taken as an elective.