This subject introduces students to cloud computing concepts and the fundamental concepts of machine learning. Students will learn the security and architecture of cloud platforms, and a range of model-based and algorithmic machine learning methods, including regression and classification. Students will also gain a solid understanding of leveraging cloud platforms to label, build, train, and deploy a custom machine learning model through a guided, hands-on approach.

This subject aims to equip learners with practical skills and knowledge related to data collection, storage, preparation, analysis, and visualisation tools commonly used in analytics and machine learning (ML) applications. As part of the course, students will delve into case studies derived from real-world scenarios. This exposure will empower them to make well-informed decisions when constructing data pipelines tailored to their specific applications. The course is structured to provide a balanced mix of theoretical knowledge and hands-on experience to ensure an in-depth understanding of data engineering concepts.

This subject introduces the fundamental concepts of deep learning neural network and Natural Language Processing. It covers concepts of several deep learning methods such as Convolutional Neural Network, Recurrent Neural Networks and Transformers that can be applied to fields such as computer vision and natural language processing. Students will learn cloud-based deep learning solutions by leveraging cloud platforms for training and deployment, implementing these solutions to different problems using cloud machine learning services.

This is an immersive subject designed to provide learners with hands-on experience in leveraging generative artificial intelligence techniques. Participants will learn to train Large Language Models (LLMs) using custom data, cultivating a deeper understanding of their structure and parameters. They will gain practical skills in deploying image generation services on the cloud using existing models such as Stable Diffusion. Additionally, the course will cover prompt engineering and help learners understand prompt structures to optimise generative outputs. By the end of the course, participants will be equipped with the knowledge and tools to create and deploy their own custom generative AI models in a cloud environment, expanding their creative possibilities.