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.

MLOps is about supporting and automating the assessment of model performance, model deployment and the following monitoring. Valuable tools for an effective MLOps process are data version trackers, model registries, feature stores, and experiment trackers.

During the workshop, we will showcase the challenges of “applied” machine learning and the value of MLOps with a practical case study. We will develop an ML model following MLOps best practices, from raw data to production deployment. Then, we will simulate a further iteration of development, resulting in better performance, and we will appreciate how MLOps allows for easy comparison and evolution of models.

AWS will provide the tools to effectively implement MLOps: the workshop is also intended to offer an overview of the main resources of the cloud platform and to show how they can support model development and operation.

Held at Applied Machine Learning Day, 26th March 2022, Lausanne, Switzerland
Repo: https://github.com/xtreamsrl/amld22-mlops-on-aws