awesome-mlops-kubernetes and awesome-ml-experiment-management
These are complementary tools, with one providing a general framework for MLOps on Kubernetes and the other specializing in the crucial aspect of experiment management within that broader MLOps ecosystem.
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.
About awesome-ml-experiment-management
awesome-mlops/awesome-ml-experiment-management
A curated list of awesome open source tools and commercial products for ML Experiment Tracking and Management 🚀
When you're developing machine learning models, you often run many experiments with different datasets, model architectures, and parameters. This resource helps you keep track of all those experiments, including the inputs, configurations, and results, so you can easily compare them and understand what worked best. It's for anyone involved in developing and iterating on machine learning models, from individual data scientists to ML engineering teams.
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