Software Engineering for Machine Learning are techniques and guidelines for building ML applications that do not concern the core ML problem -- e.g. the development of new algorithms -- but rather the surrounding activities like data ingestion, coding, testing, versioning, deployment, quality control, and team collaboration. Good software engineering practices enhance development, deployment and maintenance of production level applications using machine learning components.
These resources cover all aspects.
How to manage the data sets you use in machine learning.
How to organize your model training experiments.
How to deploy and operate your models in a production environment.
How to organize teams and projects to ensure effective collaboration and accountability.
Tooling can make your life easier.
We only share open source tools, or commercial platforms that offer substantial free packages for research.
Contributions welcomed! Read the contribution guidelines first