Awesome Software Engineering for Machine Learning AwesomePRs Welcome

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.

⭐ Must-read

πŸŽ“ Scientific publication

<br> Based on this literature, we compiled a survey on the adoption of software engineering practices for applications with machine learning components.

Feel free to take and share the survey and to read more!


Broad Overviews

These resources cover all aspects.

Data Management

How to manage the data sets you use in machine learning.

Model Training

How to organize your model training experiments.

Deployment and Operation

How to deploy and operate your models in a production environment.

Social Aspects

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