Koldim2001/MLflow_tracking
Использование MLflow для трекинга экспериментов PyTorch и Sklearn
This project helps machine learning engineers and researchers manage their experiments by tracking model parameters, metrics, and artifacts like model weights during training. It takes your PyTorch and Scikit-learn model training runs as input and provides a searchable history of experiment details, allowing for easy comparison and reproducibility.
Use this if you are a machine learning practitioner who needs to systematically record and compare the results of different model training runs.
Not ideal if you are looking for a fully managed, cloud-based MLOps platform with advanced deployment and monitoring features.
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30
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12
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Jupyter Notebook
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Last pushed
Dec 25, 2025
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