This module is a core module for the MSc in Artificial Intelligence. The aim is to prepare students to understand and use a wealth of new technologies around the sharing, composition, reuse, and retrieval of algorithms and data pertaining to the production and use of Artificial Intelligence as well as the guarantees of system-level correctness, compliance and quality.

The module starts by teaching you all aspects of one of the most prominent and well-supported AI ecosystems in the world with practical activities and tests. The proposed delivery covers the following topics: Cloud Ecosystem Foundations' introduction 1 Cloud Concepts Overview 2 Cloud Economics and Billing 3 Global Infrastructure Overview 4 Cloud Security 5 Networking and Content Delivery 6 Compute 7 Storage 8 Databases 9 Cloud Architecture 10 Automatic Scaling and Monitoring With the cloud ecosystem content completed, the student will learn how to develop AI/ML applications within the context of an advanced AI ecosystem 1 Machine Learning Foundations objectives, roles and resources in an advanced AI ecosystem 2 Introducing Machine Learning in the context of an advanced AI ecosystem 3 Implementing a Machine Learning pipeline with an advanced AI ecosystem's workflow tool 4 Forecasting 5 Computer Vision (CV) 6 Natural Language Processing (NLP) 7 Generative AI 8 Wrap-up The module includes collaborative project work and the submission of a project report based on a system built by the student. The module will equip the student with the knowledge and experience of advanced AI/ML ecosystems, allowing them to tackle problems using AI/ML at a global scale.

On successful completion of this module, students will 1. Demonstrate an understanding of the system-level interplay of collaborating tools and platforms that co-deliver a complex AI-enhanced application. 2. Analyse and evaluate workflows and processes that use Artificial Intelligence-related tools and techniques with respect to the criteria introduced in the theoretical part of the module 3. Apply the criteria to scientific computing tools, programming languages and host platforms introduced in previous modules. 4. Demonstrate that they know the criteria and the mindset behind the requirements and the delivery of "assurance" and "quality", especially in face of heterogeneity of components, technologies and platforms in complex ecosystems that deliver AI-enhancement or support. 5. Apply the learned concepts, tools and techniques to a project

On successful completion of this module, students will 1. Demonstrate an understanding of, and formulate, rigorous qualities of systems: the "ethics" of the responsibility for quality. 2. Undertake and reflect on interaction (discussion, collaboration and agreement) and teamwork in the practical components of the module.

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The module will be delivered using a blended learning approach using lectures, labs and tutorials. A project will support the practical part.