Skip to content

Our Projects

Software Engineering practices for Machine Learning

When it comes to transitioning AI systems from experimental environments into business settings, there is a huge gap between theory and practice. An solution must bring value to the organization and fit within the organisation’s IT strategy and their established processes concerned with software development, maintenance, data governance, evolution etc. These solutions must also leverage existing IT and data assets, comply with the relevant regulations, satisfy ethical considerations etc

This project will need a rethink of many traditional software engineering practices e.g.:

  • software architecture: efforts so far have mostly been focused on data engineering of lakes and data warehouses for reporting and statistical modelling. AI/ML requires new modelling approaches that allow quick development and maintainability (e.g. low-code approaches) as well as the ability to efficiently access and process production data. The right architecture should not only satisfy requirements but also leverage AI, cloud and digital transformation technologies.

  • development processes: today’s SDLC and agile methods used within industry need to evolve to fully cater for the experimental data driven nature of AI/ML projects. This project will consider using emerging SoftwareEngineering4AI (SE4AI) techniques.

  • requirements engineering: problems should not be expressed in terms of an ML task but as a set of business objectives with associated measures such as competitiveness, successfulness, and financial benefits​. Capturing requirements such as how to achieve transparency (explanations), usability (UI Design, UI aids), trust and performance (information quality) all at the same time represent a difficult challenge.

These issues are all interlinked e.g. adding business objectives may reduce the usability and decrease performance, adding more transparency may obscure and decrease trust, adding more usability may decrease performance etc. In some cases, ethical and compliance with regulations are other important considerations that need to be taken into account when developing the system.  

This project will focus on development practices that provide the ability to “personalise” AI/ML in different contexts using new approaches such as AutoML and collaborative “code-free” technologies. As a case study, this project will investigate how to design infrastructures that allow the use of ML techniques for analysing IoT timeseries data (e.g. indoor/outdoor air temperature data, indoor/outdoor air quality data, relative humidity data) for the purpose of monitoring compliance with building regulations (e.g. WELL standard). 

By working with us, we can reach your goals in a short distance.