ml-engineering

The ml-engineering repository addresses the complexities of machine learning workflows, providing engineers with a structured approach to debugging and optimising AI models. It is particularly beneficial for ML/AI teams, including data scientists and machine learning engineers, who require a reliable framework for large language models and GPU utilisation. With a growth of 998 stars (6.4%) over the past 96 days, it demonstrates healthy adoption, indicating that it meets the needs of its users effectively. This open source tool is production-ready, but teams should avoid it if they require a highly specialised solution or have unique infrastructure constraints.

This repository is a strong choice when teams need a comprehensive open source tool for engineering teams focused on machine learning and AI projects. Consider alternatives if your project requires a more niche approach or specific integrations that this tool does not support.

This repository is ideal for machine learning engineers, data scientists, and AI researchers. It is often included in products and systems that involve AI model development, deployment, and optimisation.

Latest commit: 2026-02-13. Over the past 97 days, this repository gained 998 stars (+6.4% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.