clemens33/mlflow
mlflow container setup for docker, docker compose and kubernetes including helm chart
This project helps MLOps engineers and data scientists streamline their machine learning experiment tracking and model artifact management. It provides a pre-configured setup to log experiment parameters, code versions, metrics, and output files, which are then accessible through a central UI. The user provides their models and experiment data, and the system organizes and stores the results for better reproducibility and collaboration.
Use this if you need a robust, containerized environment to track machine learning experiments and manage model artifacts consistently across various infrastructure setups like Docker or Kubernetes.
Not ideal if you are looking for a simple, single-script solution for ad-hoc model training without the need for persistent tracking or collaboration features.
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11
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2
Language
Shell
License
MIT
Category
Last pushed
Mar 06, 2026
Commits (30d)
0
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