Applying machine learning in the real world is hard: reproducibility gets lost, datasets are dirty, data flows break down, and the context where models operate keeps evolving. In the last 2-3 years, the emerging MLOps paradigm provided a strong push towards more structured and resilient workflows.

In this talk, we show how to build custom models using SageMaker and its MLOps facilities. We will explore modules such as the experiment tracker, model register, feature storage, and model deployment. As a case study, we will use the forecasting of the Italian power load.

First delivered at AWS User Group Meetup, 18 April 2023, Milan, Italy