Integrating DevOps practices into ML-driven systems: A Framework and Maturity Model for Continuous Machine Learning Development
About the Project
The rapid adoption of Machine Learning (ML) across industries has transformed how systems are built, deployed, and maintained. However, the operationalisation of ML—commonly referred to as MLOps—still faces significant challenges related to reproducibility, scalability, monitoring, and collaboration between data science and engineering teams. While DevOps has long-established principles and practices for continuous integration, delivery, and deployment (CI/CD) in traditional software engineering, its seamless integration into the ML lifecycle remains underdeveloped. Bridging this gap requires a systematic framework that adapts DevOps methodologies to the specific needs of ML systems.
This PhD project aims to explore and formalise the integration of DevOps principles within ML-driven systems, resulting in a unified framework that supports Continuous Machine Learning Development. The research will analyse how established DevOps practices can be extended to accommodate ML-specific workflows. In doing so, it will identify the critical interfaces between data, model, and deployment pipelines, guiding how to achieve faster iteration, improved reliability, and reduced technical debt in ML-driven systems.
The primary objectives of this research are to: 1) develop a conceptual framework that unifies DevOps and MLOps practices into a coherent lifecycle for continuous ML development; 2) propose a maturity model to assess organisational readiness and capability in implementing integrated DevOps–MLOps pipelines; and 3) to validate the framework through case studies and experimental prototypes, demonstrating its effectiveness in improving ML system workflows.
Academic qualifications
First degree (minimum 2:1 classification) in Computer Science
English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted.
Essential attributes:
- Experience in fundamental software engineering
- Competent in one (or some) programming languages
- Machine Learning skills
- Knowledge of DevOps and MLOps principles
- Good writing and communication skills
Desirable attributes:
- Statistics skills
APPLICATION CHECKLIST
- Completed application form
- CV
- 2 academic references, using the Postgraduate Educational Reference Form (download)
- Research project outline of 2 pages (list of references excluded). The outline may provide details about
- Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
- Research questions or objectives.
- Methodology: types of data to be used, approach to data collection, and data analysis methods.
- List of references.
- The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
- Statement no longer than 1 page describing your motivations and fit with the project.
- Evidence of proficiency in English (if appropriate)
To be considered, the application must use
- the advertised title as project title
For informal enquiries about this PhD project, please contact s.rafi@napier.ac.uk
Applications accepted all year round
Self-Funded PhD Students Only
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