awesome-ml-pipelines and awesome-mlops-kubernetes
These two tools are complements, as awesome-mlops/awesome-ml-pipelines focuses on general machine learning and data science workflows, while awesome-mlops/awesome-mlops-kubernetes specifically curates tools for deploying and managing those machine learning pipelines within a Kubernetes environment.
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-mlops-kubernetes
awesome-mlops/awesome-mlops-kubernetes
A curated list of awesome open source tools and commercial products that will help you train, deploy, monitor, version, scale, and secure your production machine learning on kubernetes 🚀
Managing and operating machine learning models in a production environment can be complex. This project helps ML engineers and data scientists discover tools to streamline tasks like training, deployment, monitoring, and version control for their machine learning models. It provides a curated list of solutions that integrate with Kubernetes infrastructure.
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