CSMASC: AI Software on the Cloud

Module code: CSMASC

Module provider: Computer Science; School of Mathematical, Physical and Computational Sciences

Credits: 20

Level: 7

When you’ll be taught: Semester 2

Module convenor: Dr Lily Sun , email: lily.sun@reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2025/6

Available to visiting students: Yes

Talis reading list: Yes

Last updated: 16 April 2025

Overview

Module aims and purpose

This module introduces the fundamentals of artificial intelligence and software engineering, with a focus on cloud environments. It highlights the critical knowledge and skills needed to design, optimize, configure, and manage the development of AI-powered applications in the cloud.

Students will establish understanding of how cloud services support scalable AI solutions, automate software engineering tasks, and enable efficient deployment in response to the fast-changing and AI-advancing world in the digital ear. This course is structured to support students transitioning into the field, emphasizing core concepts, practical applications, and hands-on projects with minimal prior technical experience required.

Students will also be able to demonstrate their professional skills of:

• Creative problem-solving and critical thinking;
• Communication and team-work; and
• Professional and effective writing for requirement documents and project reports; and
• Appropriate, effective, and ethical use of Generative AI technologies to enhance learning.

Module learning outcomes

By the end of the module, it is expected that students will be able to:

1. Understand foundational AI and software engineering principles relevant to cloud services;
2. Develop skills in using cloud-native tools (e.g., MLOps) to support efficient and streamlined software development and deployment workflows, including tasks such as continuous integration, automated deployment, scaling, and monitoring;
3. Optimize AI technologies for configuration within cloud-based software solutions, ensuring effective integration of selected software modules to meet requirement specifications;
4. Acquire hands-on experience with AI-aids and cloud services for deploying software applications for high work efficient and productivity;
5. Evaluate the ethical, privacy, and security aspects of deploying AI applications on cloud; and
6. Verify the application of requirements methods, techniques, and quality management for meeting stakeholders’ needs and expectations.

Module content

The module will cover the following topics:

• Introduction to Cloud Computing and its relevance for AI and software engineering
  • cloud computing basics: IaaS, PaaS, SaaS, and AIssS models
  • overview of major cloud platforms (AWS, Azure, Google Cloud) and their AI services
• Foundations of AI in cloud environments
  • recap the core AI concepts: machine learning, data analytics, and model deployment
  • overview of AI services (e.g., image recognition, natural language processing, speech-to-text), and pros and cons using pre-trained AI models on the cloud
• Basics of Software Engineering for Cloud-Based AI
  • software engineering principles for adopting and managing cloud services
  • introduction to cloud-native applications of serverless computing (i.e., microservices) and its architecture for modular AI solutions
  • use of LLM to aid formulating requirements specifications, ensuring AI-capabilities integrated in existing software applications or plugged in to existing software applications via gateway interfaces on the cloud
• Performing cloud AI Services and APIs
  • overview of cloud AI services: Google Vision API, AWS Rekognition, and Azure Cognitive Services
  • simple use cases: integrating AI capabilities like language translation, image analysis, and sentiment analysis into applications
• Data management and security for cloud-based AI
  • basics of data storage on the cloud: data lakes, databases, and file storage
  • introduction to data privacy, security, and ethical considerations, such as responsible AI usage, data protection, and bias in AI models
• Basics of MLOps (Machine Learning Operations) for deployment and monitoring
  • introduction to version control, continuous integration, and automated deployment of AI models for software solutions
  • cloud tools for MLOps (e.g., AWS SageMaker, Azure ML, Google AI Platform)

Structure

Teaching and learning methods

This module will take a problem-based learning approach. Lectures will introduce students the software engineering theories and tools specified in Module Content. Students will be supervised through a series of practical sessions to apply the software engineering knowledge and skills in a given problem context and develop a technical solution. There will also be learning materials in digital forms when they are required to support learning.

There are two types of assessment (i.e. formative assessment and summative assessment) which will support and reinforce students’ learning. A formative assessment is carried out through weekly learning activities. Summative assessment consists of two pieces in the forms of written coursework assignment. Appropriate feedback will be timely communicated with students for enhancing learning.

Study hours

At least 48 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 50% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Level 3 (i.e. foundation modules for Part 0) and Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Each topic in a week has defined learning tasks which will enable students to self-reflect on the learning. Each practical session in a week will be severed as to facilitate the learning with personalised feedback provided towards the overall learning in this subject.

Outcomes of the formative assessment for each topic may be shared in the first 30 mins in a group during the tutorial session when appropriate.

Reassessment

Additional costs