CS3CS: Cloud-based AI Solutions

Module code: CS3CS

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

Credits: 20

Level: 6

When you’ll be taught: Semester 2

Module convenor: Dr Xiaomin Chen , email: xiaomin.chen@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: 3 April 2025

Overview

Module aims and purpose

This module introduces the fundamentals of applying software engineering principles to create AI solutions on cloud environments. It highlights the knowledge and skills needed to design, develop, optimise and deploy AI systems that are efficient, reliable and scalable using cloud-based services.

Students will establish an understanding of the concept Artificial Intelligence as a Service (AIaaS) on cloud platforms and learn technical skills of packaging pre-built AI capabilities along the AI pipeline into a customised AI solution, in response to the application requirements of accuracy, efficiency and scalability.

This module is structured to support students pursuing a career in AI engineering, emphasising core concepts, practical applications, and hands-on exercises, assuming basic prior knowledge in Machine Learning and Artificial Intelligence.

Module learning outcomes

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

1. Understand the concept of AI as a Service (AIaaS) and the core principles of AI engineering on cloud platforms;
2. Apply AI engineering techniques to optimise the selection and integration of AI capabilities within cloud-based services, ensuring optimal performance and alignment with requirements specifications;
3. Deploy intended AI solutions using cloud-native tools (e.g., Microsoft Azure AI), including data preprocessing, feature engineering, model selection, hyperparameter tuning, benchmarking and performance evaluation; and
4. Evaluate the ethical, privacy, and security aspects of deploying AI applications on cloud.

Module content

Ìý

The module will cover the following topics:

• Introduction to Cloud Computing and its relevance for AI Engineering
• Overview of cloud computing models: IaaS, PaaS, SaaS, and AIaaS
• Major cloud platforms (AWS, Azure, Google Cloud) and their AI services
• Pros and cons using pre-trained AI capabilities on the cloud
• AI Foundations
• Recap of core AI concepts and techniques
• AI deployment pipeline
• Key AI applications (e.g., prediction, regression, image recognition, natural language processing).Ìý
• Techniques of AI Engineering
• ÌýEnterprise Requirements Analysis (e.g. CI/CD pipelines for ML, A/B Testing & Canary Deployments)
• Pipeline Optimisation (e.g. MLOps & continuous optimisation)ÌýBenchmarking & Performance Evaluation (e.g. real-time performance tracking and monitoring, cloud cost optimisation)
• Developing with Cloud AI Services and APIs
• Simple use cases: integrating AI capabilities like language translation, image analysis, and sentiment analysis into applications

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Structure

Teaching and learning methods

This module will take a problem-based learning approach. Lectures will introduce students the AI engineering theories and cloud AI tools specified in Module Content. Students will be supervised through a series of practical sessions to apply the AI 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. Appropriate feedback will be timely communicated with students for enhancing learning. Summative assessment takes the form of written coursework assignment.

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

• Creative problem-solving and critical thinking;Ìý
• Communication and team-work;ÌýÌý
• Professional and effective writing for requirements documents and project reports.Ìý

Study hours

At least 44 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 40% 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 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.Ìý

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