awesome-production-machine-learning and awesome-mlops
These are complementary curated reference lists that together cover overlapping but distinct aspects of ML production—the first focusing on practical open source libraries for deployment and monitoring, while the second provides a broader MLOps reference guide that likely includes both tools and conceptual frameworks.
About awesome-production-machine-learning
EthicalML/awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
This is a curated collection of open-source tools and libraries designed for machine learning engineers and MLOps practitioners. It helps you find solutions to deploy, monitor, version, scale, and secure your machine learning models in production environments. You can quickly navigate through various aspects of the ML lifecycle, from data pipelines to model serving, to build robust and efficient ML systems.
About awesome-mlops
visenger/awesome-mlops
A curated list of references for MLOps
This is a curated collection of resources designed to help machine learning engineers, data scientists, and product managers effectively deploy and manage machine learning models in real-world applications. It provides links to articles, courses, books, and communities that explain how to take a trained machine learning model and integrate it into operational systems, ensuring reliability and continuous improvement. The goal is to provide a comprehensive guide for anyone looking to bridge the gap between developing ML models and running them successfully in production environments.
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