Software Engineering for Machine Learning
Leiden Institute of Advanced Computer Science (LIACS), The Netherlands
An ever-increasing number of organisations are developing applications that involve machine learning (ML) components. The complexity and diversity of these applications calls for engineering techniques to ensure they are built in a robust and future-proof manner.
On this website we collect, validate and share engineering best practices for software including ML components. To this end, we study the scientific and popular literature and engage with machine learning practitioners.
For more information access our catalogue of ML engineering best practices or read our annual report on the State of Engineering Practices for Machine Learning.
Our paper “AutoML adoption in ML Software” was accepted at the 8th ICML Workshop on AutoML.
Our paper “Practices for Engineering Trustworthy Machine Learning Applications” was accepted at WAIN’21@ICSE’21.
We published a report on the “2020 State of AutoML Adoption”. Read more about it here!
We published a report on the “2020 State of Engineering Practices for Machine Learning”. Read more about it here!
Our paper “Adoption and Effects of Software Engineering Best Practices in Machine Learning” was accepted at ESEM 2020.