ml-experiment-tracking
Track and manage ML experiments with MLflow, Weights & Biases, Neptune, and CometML. Covers experiment logging (hyperparameters, metrics, artifacts), auto-logging for PyTorch, TensorFlow, scikit-learn, and XGBoost, experiment comparison and visualization, artifact versioning, model lineage, reproducibility (environment, code, data versioning), remote tracking server setup, team collaboration, experiment search and querying, and integration with training pipelines. Use when running experiments, comparing model versions, or setting up experiment infrastructure.
- Version
- 1.0
- License
- Apache-2.0
Pinned to revision 45cf0fa3c5e7, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/ml-experiment-tracking/SKILL.md
- skills/ml-experiment-tracking/references/REFERENCE.md
- skills/ml-experiment-tracking/scripts/experiment_compare.py
- skills/ml-experiment-tracking/scripts/mlflow_tracker.py
Every link opens the file at its source, pinned to the revision this page describes.