Module manager: Dr Abdulrahman Altahhan
Email: A.Altahhan@leeds.ac.uk
Taught: 1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June, 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr) View Timetable
Year running 2026/27
This module is not approved as an Elective
This module introduces the principles and practices of machine learning operations (MLOps), focusing on how machine learning models are developed, deployed, and managed in production-oriented settings. It covers the end-to-end lifecycle of artificial intelligence (AI) systems, including workflow automation, version control, testing, and monitoring, alongside computational aspects such as GPU acceleration and high-performance computing for efficient training and experimentation. Students gain practical experience with tools and frameworks commonly used in production environments to build reproducible, scalable, and maintainable AI workflows.
This module aims to develop the knowledge and practical skills required to operationalise machine learning systems in reliable, efficient, and reproducible ways. It introduces the principles of the MLOps lifecycle, from experimentation and model tracking to deployment, monitoring, and continuous improvement. Students explore how automation, version control, and testing ensure robustness and how computational resources such as GPUs and high-performance computing support scalable experimentation. Learning activities combine conceptual explanations, guided walkthroughs, and practical exercises that simulate production-oriented workflows using commonly adopted tools and frameworks. Through these activities, students gain confidence in designing, managing, and optimising end-to-end machine learning pipelines that bridge development and operational practice.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Explain the principles and lifecycle stages of MLOps and their role in managing end-to-end AI workflows.
2. Apply tools and frameworks for experiment tracking, version control, automation, and reproducibility in machine learning projects.
3. Design and implement machine learning pipelines that integrate data processing, model training, evaluation, and deployment workflows.
4. Utilise GPU and high-performance computing resources to optimise model training and experimentation at scale.
5. Assess the performance, reliability, and maintainability of machine learning systems through monitoring, testing, and iterative improvement.
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Apply systematic problem-solving and workflow management skills to deploy, monitor, and maintain machine learning models effectively.
2. Demonstrate adaptability and continuous learning by integrating new tools, frameworks, or methodologies into operational pipelines.
3. Communicate technical processes and outcomes clearly to both technical and non-technical stakeholders, using appropriate documentation and visualisation.
4. Apply integrated problem-solving and systems thinking to develop reliable end-to-end machine learning pipelines.
5. Exercise reflective practice and critical evaluation to identify bottlenecks, optimise processes, and improve operational efficiency.
Indicative content for this module includes:
· Principles and lifecycle of MLOps
· Version control, experiment tracking, and reproducibility in AI workflows
· Automation of machine learning pipelines and workflow orchestration
· Model evaluation, validation, and continuous integration for machine learning systems
· Deployment concepts and tools used in production-oriented environments
· Monitoring, model drift detection, and iterative model improvement
· Use of GPU acceleration and high-performance computing for scalable training and experimentation
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Discussion forum | 6 | 1 | 6 |
| WEBINAR | 6 | 1 | 6 |
| Independent online learning hours | 42 | ||
| Private study hours | 96 | ||
| Total Contact hours | 12 | ||
| Total hours (100hr per 10 credits) | 150 | ||
1. Webinar-Based Discussion and Q&A
2. Weekly Practical Exercises
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Online Assessment | ~20 questions about different scenarios | 20 |
| Coursework | Technical Report | 80 |
| Total percentage (Assessment Coursework) | 100 | |
This module will be reassessed through a 100% individual assessment in the same format as Assessment 2 (coursework project). The reassessment will involve a practical project that requires students to apply and integrate the knowledge and skills developed across all learning outcomes.
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
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