DevOps Intelligence AI-Optimised Development

Unit code: NIT3013 | Study level: Undergraduate
Credit points
12
(Generally, 1 credit = 10 hours of classes and independent study.)
Location
Footscray Park
Online Real Time
VU Brisbane
VU Sydney
Prerequisites
(Or equivalent to be determined by unit coordinator)
Co-requisites
(Corequisite units must be studied concurrently with this unit)
Overview
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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:

  1. Design and implement AI-powered DevOps pipelines for automated software deployment and operations management.
  2. Implement AI-driven monitoring and predictive analytics to optimise system performance and resource allocation.
  3. Experiment AI-enhanced security measures to detect, prevent, and mitigate security risks in DevOps workflows.
  4. Automate cloud infrastructure management using AI-based orchestration tools for scalability, fault tolerance, and self-healing capabilities individually and as a part of team.
  5. Critically analyse real-world case studies of AI-driven DevOps strategies and their impact on software development efficiency.

Assessment

For Melbourne campuses

Assessment type:
|
Grade:
Lab Practical Test on AI-Assisted Development Exercise
Assessment type:
|
Grade:
Assignment on AI-Optimised DevOps design and Implementation (Group) with presentation, demonstration and Q&A verification.
Assessment type:
|
Grade:
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.

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