top of page
City Skyline

Cloud computing and


Please note to take this course you must first have completed  Programming in Python

This course introduces cloud computing and MLOps. It covers the basics of cloud computing from a user point-of-view as well as the principles and practices of MLOps, including model training, deployment, and monitoring. The focus is on giving practical knowledge to participants which gives them the right tools to deploy, test and maintain data science models at scale and structure data science projects correctly. 

This module can be taken alone or as part of a PG Certificate, PG Diploma or Full Masters Program. 

Key Skills

By the end of this course, participants should have some knowledge and understanding of: 

Cloud Computing Proficiency: Ability to utilise cloud computing services in data science projects.

Deployment of Machine Learning Models: Skills to deploy, test, and maintain machine learning models.

Cloud Service Utilisation: Proficiency in using cloud services for data science applications.

Machine Learning Model Maintenance: Knowledge and skills to maintain machine learning models over time.

Practical Application: Applying cloud computing and machine learning skills to real-world projects.

Testing Machine Learning Models: Ability to rigorously test machine learning models for reliability.

Cloud Resource Management: Efficiently managing and optimising resources in cloud environments.

Data Science Project Implementation: Integrating cloud computing and machine learning into successful data science projects.

Desired Skills

By the end of this course, students should be able to: 

Understand the basics of cloud computing and its applications.

Compare and contrast the different types of cloud computing services.

Apply the principles and practices of MLOps to train, deploy, and monitor machine learning models in the cloud.

Use popular cloud computing platforms, such as AWS and Azure, for MLOps.



Cloud computing and MLOps is an elective 10 credit course and therefore students are expected to input approximately 100 hours of study into the course.


The total number of contact hours is 15 hours. This leaves 85 hours for private study.

There will be an assessment at the end of the course 



This module consists of 2 - hour lectures per day for 5 days, plus a 1 - hour tutorial per day. 

 There will be optional clinics on the last day of the course.   

The dates of each lecture are confirmed closer to the start of each term. If you have any questions about dates, please contact


bottom of page