Module aims
We’ll cover the engineering concepts required to build robust and trustworthy systems that make use of machine learning. We’ll look at all aspects of systems, from data ingestion to user experience, while considering the influence of regulation and wider society. We’ll spend half of our time in lectures, and half in the lab. Over the course of the module, the lab and coursework we will implement, and reliably operate, a simplified machine learning based system in a simulated environment inspired by a real-world problem.
We will not cover the design of machine learning models themselves, we’ll focus on the systems that surround and support them.
Learning outcomes
Upon successful completion of this module you will be able to:
* Train a machine learning model, and integrate it into a user facing application.
* Build a system that is robust in the presence of common data-centric failure modes.
* Safely evolve systems in response to changing requirements.
* Make reasoned design choices for systems handling sensitive data.
* Adapt technical designs to regulation and the need to maintain societal trust.
* Critique and assess elements of user experience design related to ML systems.
Module syllabus
We’ll cover the engineering theory and skills specific to machine learning systems, including:
* The differences between software systems that make use of machine learning, and those that don’t.
* Architectures for training models, and the influences on them.
* Architectures for model inference, and the influences on them.
* Monitoring and reliability in machine learning systems.
* Evolving a running machine learning system.
* The social context of machine learning, and its influence on system design – including frameworks for responsible engineering and privacy preserving technologies.
* The legal context of machine learning, and its influence on system design – including GDPR and ISO-13485.
* The impact of user experience design on the underlying system design.