Module Code: H9BOC
Long Title Doing Business on the Cloud
Title Doing Business on the Cloud
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: COLETTE DARCY
Module Author: COLETTE DARCY
Departments: School of Business
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Discriminate between the operating principles of cloud computing and other information management technologies, to assess their application as business solutions.
LO2 Analyse the role of cloud computing and AI-enhanced services in modern organisational configurations, including the application of machine learning models and data analytics.
LO3 Determine legal and commercial implications of adopting cloud computing for certain business processes.
LO4 Develop a reflective approach to analyse and evaluate organisational change situations related to the adoption of cloud computing and AI technologies, considering ethical and societal impacts.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

There are no additional entry requirements for this module.  The programme entry requirements apply.  No pre-requisites or co-requisites apply.  
 

Module Content & Assessment

Indicative Content
The Continuing Evolution of Cloud Computing:
A review of the history and foundational concepts of cloud computing integrated with artificial intelligence. Exploring the synergies and challenges of merging AI with cloud computing. How AI-powered cloud services are shaping industries like healthcare, finance, and retail.
The Anything as a Service (XaaS) Concept:
Understanding of how AI capabilities like machine learning, natural language processing, and data analytics are being integrated into Anything as a Service (XaaS) models: AIaaS, MLaaS, and DAaaS (Data Analytics as a Service). Exploration of emerging trends like federated learning and edge AI in the cloud.
The Legal and Commercial Factors:
Data Protection Considerations. Roles and responsibilities. Typical considerations for agreements between cloud providers and customers. Considerations for public sector and sensitive data. The commercial models that typically apply to cloud. Licensing and pricing models. Ethical concerns like AI bias, data privacy, and algorithmic accountability in the cloud.
Appraisal of Leading Solutions:
Application of AI-powered cloud tools such as conversational AI platforms, generative text tools, graphic design software with AI features, and online IDEs, to explore the practical use and synergies between cloud computing and artificial intelligence.
Evaluation and Selection:
Emphasis on AI-based tools for evaluating cloud services, including predictive analytics for cost estimation and AI-driven frameworks for security assessment.
Implementation:
Detailed stages in AI-powered cloud implementation, including AI project management frameworks. The evolving role of consultants and external vendors who specialize in AI. Practical project involving deploying an AI model on a cloud service, including fairness and transparency testing. Learners will be provided with trial licences for an AI-integrated cloud platform to execute their project.
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: 100
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
Learning outcomes 1 – 4 are reached in two stages. First course content is reviewed, discussed and worked out in the form of a framework to solve a real-life business situation. Secondly, that formulation work is applied to the problem and a solution is presented, consolidating learning.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
Learners must pass the module. Those learners who fail to pass on the first attempt will be given a further opportunity to do so on foot of detailed feedback. The repeat assessment will be noted as a second sitting.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom and demonstrations 30 Per Semester 2.50
Directed Learning Directed e-learning 30 Per Semester 2.50
Directed Learning Independent learning 65 Per Semester 5.42
Total Weekly Contact Hours 10.42
 

Module Resources

Recommended Book Resources
  • Lisdorf, A. (2021), Cloud Computing Basics: A non-technical introduction, Apress.
  • Rhoton, J.. (2013), Cloud Computing Explained: Handbook for Enterprise Implementation, 2nd Ed. Recursive Press.
  • DAVID M. PATEL.. (2023), ARTIFICIAL INTELLIGENCE & GENERATIVE AI FOR BEGINNERS: The Complete Guide, 1st. Independently published. I, [ISBN: 979-8850705527].
  • Eric Lamarre,Kate Smaje,Rodney Zemmel. (2023), Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI, 1st. John Wiley & Sons, p.407, [ISBN: 978-1394207114].
Supplementary Book Resources
  • Cohen, J.A.. (2019), Cloud Computing for Every Business: Getting the Most Out of Your Technology Spending, Independent Publishing.
  • Marinescu, D. (2017), Cloud Computing: Theory and Practice, Morgan Kaufmann Publishing.
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: