PROGRAMME / 04
Master · Cloud Computing and AI Engineering
Connect cloud architecture, distributed computing and deployment to build AI platforms, manage compute resources and deliver reliable services.
For applicants with relevant prior study. Develop advanced technical and research skills. Planned workload: 120 credits.
YEAR 01Large-Scale AI Infrastructure
- Advanced Cloud Architecture
- Data Platforms
- Infrastructure Security
- Distributed Training
- GPU Resource Scheduling
- Research Methods
YEAR 02Efficient and Reliable AI Platforms
- Large Model Inference Optimisation
- AI Platform Engineering
- Cloud–Edge Computing
- Reliability and Disaster Recovery
- Compute Costs and Green Computing
- Master’s Dissertation and Defence
SHARED LEARNING
Shared foundations. Specialist depth.
Selected networking, cloud, security, deployment and monitoring modules may be shared with related PTI programmes.
Specialist focus: AI training, inference services and compute platform engineering.
Assessment and outcomes
Assess reproducible deployment, correctness, performance, security, recovery and costs. Submit architecture documentation, deployment files, test records and resource cost analysis.
Deploy a cloud AI application with model serving, version control, monitoring, scaling and recovery mechanisms.