awesome-production-machine-learning

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

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+542

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💡 Why It Matters

The awesome-production-machine-learning repository addresses the need for engineers to efficiently deploy, monitor, version, and scale machine learning models. It serves as a comprehensive resource for ML/AI teams, offering a curated list of open source tools that enhance productivity and streamline workflows. With a steady growth of 542 stars over 96 days, it reflects a stable community interest, indicating that it is a production-ready solution with a mature ecosystem. However, teams should avoid it if they require highly specialised libraries or tools that are not covered in this curated list.

🎯 When to Use

This repository is a strong choice for teams looking for a reliable open source tool for engineering teams that need to implement machine learning solutions effectively. Consider alternatives when specific requirements demand niche tools not included in this collection.

👥 Team Fit & Use Cases

Primarily, data scientists, ML engineers, and AI researchers will find this repository beneficial. It is typically used in products and systems that involve large-scale machine learning applications, data analysis platforms, and AI-driven services.

🎭 Best For

Machine Learning and AI Engineer

🏷️ Topics & Ecosystem

awesome awesome-list data-mining deep-learning explainability interpretability large-scale-machine-learning large-scale-ml machine-learning machine-learning-operations ml-operations ml-ops mlops privacy-preserving privacy-preserving-machine-learning privacy-preserving-ml production-machine-learning production-ml responsible-ai

📊 Activity

Latest commit: 2026-02-09. Over the past 97 days, this repository gained 542 stars (+2.8% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.