| CODE | ARI3217 | ||||||||||||
| TITLE | AI Enterprise Architectures and Transformation | ||||||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||||||
| EQF/MQF LEVEL | 6 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||||||
| DESCRIPTION | This study-unit teaches students to design and implement AI solutions within enterprise architectures through a streamlined, three-part approach. It is project-driven and teams will deliver a semester-long AI Transformation Project. This study-unit focuses on foundational AI operationalization in Part 1, then specializes in semantic/knowledge layers (Part 2) and LLM/RAG systems (Part 3). The approach balances essential concepts with practical implementation. Indicative topics: - EA for AI Foundations: essential business/data/app/tech layers; core capability patterns; fundamental patterns for integrating AI into legacy estates. - Data/ML Lifecycle & MLOps essentials: basic CI/CD for ML using scaffolds; model registries; single deployment pattern; fundamental observability (performance, drift, cost) with provided monitoring frameworks. - Semantic & Knowledge Layers: knowledge graphs/ontologies as enterprise context for AI applications; ontology engineering and validation; knowledge-driven retrieval systems. - LLM/RAG Implementation: production retrieval pipelines; systematic prompt engineering and governance; comprehensive evaluation frameworks; safety mechanisms and cost management; enterpri. Study-Unit Aims: - Enable students to express AI capabilities in enterprise architecture artefacts that align with business value, using essential patterns and provided templates; - Develop competence in foundational MLOps and operational quality through a minimal but complete pipeline implementation; - Embed risk, safety, and compliance thinking in design and operation, focusing on key artefacts including model cards, risk assessments, and operational procedures; - Assist students with planning realistic roadmaps for introducing AI into legacy contexts, leveraging knowledge layers and modern AI capabilities; - Cultivate the ability to justify trade-offs to technical and business stakeholders with clarity and evidence-based reasoning. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Explain how AI fits within enterprise architecture layers, and compare patterns for integrating models/LLMs/knowledge graphs into existing systems; - Describe the end-to-end ML lifecycle and the role of MLOps (CI/CD, registries, monitoring, rollback) in ensuring safe, scalable AI; - Discuss semantic interoperability and the value of knowledge graphs/ontologies for enterprise AI; - Summarize LLM/RAG architectural patterns, including retrieval pipelines, prompt governance, evaluation, and safety considerations. 2. Skills: By the end of the study-unit the student will be able to: - Produce a target AI enterprise architecture and transformation roadmap for a given scenario, with clear capability, component mapping; - Implement a minimal but real MLOps slice: data prep, training, model registration, containerized serving, and observability (metrics for performance/drift/cost); - Evaluate and defend trade-offs (accuracy vs. latency vs. cost; build vs. buy; privacy vs. utility) orally and in writing; - Collaborate effectively in cross-functional teams, using Git, issue tracking, and documentation standards. |
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| STUDY-UNIT TYPE | Lecture, Online Learning & Tutorial | ||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Charlie Abela (Co-ord.) Joel Azzopardi Christopher Zerafa |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2026/7. It may be subject to change in subsequent years. |
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