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.
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.
We launched a catalogue of Engineering best practices for Machine Learning applications. Read more about it here!
We launched our survey on the adoption of Software Engineering practices by teams that develop applications involving Machine Learning components. Take the survey now!