A focused symposium course on the deployment of Machine Learning (ML) models as part of larger software systems. We explore the ML context and definition, the motivation and challenges behind deploying ML models, the data science methodology, the ML pipeline, and current software architectures for ML-based systems, enabling participants to understand and implement effective ML deployment strategies.

An introductory course on Machine Learning (ML). We start by defining ML from an objective perspective and continue developing each dimension of such a definition. It encompasses the interaction of data, models, and compute, always driven by the problem first and materialised in reproducible ML pipelines that follow a clear methodology. Students develop both theoretical understanding and hands-on implementation skills across the full ML pipeline.

The ADS course at the University of Cambridge guides students through the data science pipeline and emphasises the importance of prioritising the motivation and context of data science projects.