awesome-ml-pipelines and awesome-ml-serving
These tools are complements, as one curates resources for managing machine learning pipelines, and the other curates resources for serving those models in production, representing sequential stages in an MLOps lifecycle.
About awesome-ml-pipelines
awesome-mlops/awesome-ml-pipelines
A curated list of awesome open source tools and commercial products that will help you manage machine learning and data-science workflows and pipelines 🚀
This is a curated collection of tools and products that help data scientists and machine learning engineers manage and automate their complex data and machine learning workflows. It provides options for orchestrating tasks, scheduling jobs, and monitoring the entire lifecycle of a machine learning project, from data ingestion to model deployment. The resources help you build robust, repeatable, and scalable machine learning pipelines.
About awesome-ml-serving
awesome-mlops/awesome-ml-serving
A curated list of awesome open source and commercial platforms for serving models in production 🚀
This list compiles tools and platforms designed to help machine learning engineers deploy their trained models so they can be used by other applications or end-users. It covers solutions for taking a developed ML model (like a recommendation engine or an image classifier) and making it accessible through an API or a user interface. This is for machine learning engineers, MLOps specialists, or data scientists responsible for moving models from development to production.
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