Software Engineering for Machine Learning
A curated list of articles covering software engineering best practices for building production machine learning applications.
What is Software Engineering for Machine Learning?
Awesome Software Engineering for Machine Learning (Awesome SE-ML) is a curated list of articles and resources that document software engineering best practices for building and maintaining machine learning applications in production. It focuses on the surrounding engineering challenges—like data versioning, testing, deployment pipelines, and team collaboration—rather than core ML algorithms. The project aims to bridge the gap between machine learning research and industrial software engineering standards.
Machine learning engineers, data scientists, ML platform teams, and software developers building or maintaining production ML systems who need guidance on engineering best practices, tooling, and lifecycle management.
It provides a centralized, vetted, and well-organized knowledge base that saves practitioners time searching for high-quality resources on ML engineering. Unlike generic ML lists, it specifically curates content around the software engineering discipline applied to ML, highlighting must-read papers and practical guides.
Overview
A curated list of articles that cover the software engineering best practices for building machine learning applications.
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