Intelligent Cloud Systems and Architectures

Unit code: NIT2007 | 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

This unit introduces you to intelligent cloud systems, where AI-driven tools and platforms enhance cloud architecture, resource management, and application deployment. You will explore how AI-driven optimisation techniques are transforming traditional cloud systems, enabling smarter, more autonomous cloud solutions. You will examine next-generation cloud deployment models and cloud services, focusing on compute, storage, security, and monitoring services enhanced by AI. You will gain hands-on experience with AI-assisted cloud management tools for tasks like predictive scaling, automated resource allocation, real-time performance optimisation, and seamless cloud migrations: preparing you for roles in cloud engineering, DevOps, or infrastructure architecture in AI-enabled environments.

Learning Outcomes

On successful completion of this unit, students will be able to:

  1. Evaluate the influence of AI-driven tools in cloud system design, optimisation, and resource management.
  2. Design intelligent cloud architectures with AI-driven tools to automate the deployment, scaling, and resource management.
  3. Develop reliable, scalable, and secure cloud infrastructure with AI-enhanced tools.
  4. Apply AI-enhanced tools for real-time monitoring, predictive scaling, and proactive management of cloud systems.
  5. Assess cloud migration strategies, integrating AI tools to automate migration processes, minimise downtime, and ensure compliance with industry standards and security best practices.

Assessment

For Melbourne campuses

Assessment type:
|
Grade:
Hands-on labs
Assessment type:
|
Grade:
AI-Enhanced Cloud Implementation project with demonstration and Q&A verification.
Assessment type:
|
Grade:
Problem-based solution design and presentation with verified live interaction.

Project:
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

NA

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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