awesome-production-machine-learning and awesome-mlops
These are complements that serve different purposes within MLOps: the first is a comprehensive resource library covering the full ML lifecycle (deployment, monitoring, versioning, scaling), while the second is a curated index of tools themselves, making them reference materials that users would consult together to discover and evaluate production ML solutions.
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
kelvins/awesome-mlops
:sunglasses: A curated list of awesome MLOps tools
This list compiles essential tools for managing the entire lifecycle of machine learning models, from initial data preparation to deployment and continuous monitoring. It helps data scientists, machine learning engineers, and MLOps practitioners navigate the vast ecosystem of tools needed to build, deploy, and maintain robust AI solutions.
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