| Unit name | AI in the Cloud |
|---|---|
| Unit code | SEMTM0052 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Cheng |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Science and Engineering |
Why is this unit important?
This unit offers an in-depth exploration of the development and deployment of machine learning (ML) pipelines using cloud services. You will gain the skills to select appropriate ML services offered by commercial cloud providers to solve business problems. Additionally, the unit provides a comprehensive overview of natural language processing (NLP), relevant ML workflows for NLP tasks, and the emerging domain of generative AI and large language models (LLMs). You will be able to identify suitable use cases for generative AI and leverage cloud-based generative AI services effectively to address practical business challenges.
How does this unit fit into your programme of study
This is a core unit in this degree. It covers essential content on working with Machine Learning and Natural Language Processing models in the cloud that will be essential to you in the project work that follows throughout the rest of the degree. This unit builds on the content covered in the TB-1 unit “Large-Scale Data Engineering”, which becomes foundational assumed knowledge for this unit.
An overview of content
Topics covered in this unit will include:
How will students, personally, be different as a result of the unit
Throughout this unit, you will have developed a strong practical understanding of designing, deploying, and managing Machine Learning (ML) pipelines using cloud services. You will gain the confidence to identify appropriate cloud-based ML solutions for diverse business problems and will be equipped with the skills to select, implement, and optimize these services effectively. Additionally, you will acquire foundational knowledge of natural language processing (NLP) techniques and address real-world text-based challenges in the cloud.
Learning Outcomes
On successful completion of the unit, you will be able to:
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including videos, group work, practical activities and self-directed exercises.
Tasks which help you learn and prepare you for summative tasks (formative):
You are allocated a two-hour tutorial session per week. The tasks assigned directly and indirectly equip you for the summative assessments. You will receive feedback from the teaching team in the tutorials.
Tasks which count towards your unit mark (summative):
Coursework (100%) on implementing and optimising AI algorithms in the cloud for given datasets. This will assess all ILOs. You will submit an individual report (3500 words) demonstrating the whole experimental process, with in-depth analysis and findings.
When assessment does not go to plan
Re-assessment takes the same form as the original summative assessment.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. SEMTM0052).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
