ML Experiment Tracking
Managing ML experiments, metrics, parameters, and artifacts using MLflow, Weights & Biases, and best practices for reproducible ML experiments and model versioning.
npx skills add majiayu000/claude-skill-registry --skill model-experiments --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# ML Experiment Tracking > **Current Level:** Advanced > **Domain:** Data Science / ML / Experimentation --- ## Overview Experiment tracking manages ML experiments, metrics, parameters, and artifacts. This guide covers MLflow, Weights & Biases, and best practices for tracking experiments, comparing models, and ensuring reproducibility in ML development. ## Experiment Tracking Importance **Benefits:** - Reproducibility - Comparison of experiments - Collaboration - Model versioning - Hyperparameter optimization ## MLflow ### Installation ```bash pip install mlflow mlflow ui # Start UI on http://localhost:5000 ``` ### Tracking ```python # MLflow tracking import mlflow import mlflow.sklearn from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, f1_score # Set experiment mlflow.set_experiment("my-experiment") # Start run with mlflow.start_run(run_name="random-forest-v1"): # Log parameters mlflow.log_param("n_estimators", 100) mlflow.log_param("max_depth", 10) mlflow.log_param("random_state", 42) # Train model model = RandomForestClassifier( n_estimators=100, max_depth=10, random_state=42 ) model.fit(X_train, y_train) # Make predictions y_pred = m
- Overview
- Experiment Tracking Importance
- MLflow
- Installation
- Tracking
- Autologging
- Model Registry
- Projects
- Weights & Biases
- Hyperparameter Sweeps
- TensorBoard
- Metrics Logging
- Hyperparameter Tracking
- Artifact Storage
pip install mlflow mlflow ui # Start UI on http://localhost:5000
What does the ML Experiment Tracking skill do?
Managing ML experiments, metrics, parameters, and artifacts using MLflow, Weights & Biases, and best practices for reproducible ML experiments and model versioning.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill model-experiments --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.