experiment-tracking
Original：🇺🇸 English
Translated
Use when "experiment tracking", "MLflow", "Weights & Biases", "wandb", "model registry", "hyperparameter logging", "ML experiments", "training metrics"
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Sourceeyadsibai/ltk
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NPX Install
npx skill4agent add eyadsibai/ltk experiment-trackingTags
Translated version includes tags in frontmatterSKILL.md Content
View Translation Comparison →Experiment Tracking
Track ML experiments, metrics, and models.
Comparison
| Platform | Best For | Self-hosted | Visualization |
|---|---|---|---|
| MLflow | Open-source, model registry | Yes | Basic |
| W&B | Collaboration, sweeps | Limited | Excellent |
| Neptune | Team collaboration | No | Good |
| ClearML | Full MLOps | Yes | Good |
MLflow
Open-source platform from Databricks.
Core components:
- Tracking: Log parameters, metrics, artifacts
- Projects: Reproducible runs (MLproject file)
- Models: Package and deploy models
- Registry: Model versioning and staging
Strengths: Self-hosted, open-source, model registry, framework integrations
Limitations: Basic visualization, less collaborative features
Key concept: Autologging for major frameworks - automatic metric capture with one line.
Weights & Biases (W&B)
Cloud-first experiment tracking with excellent visualization.
Core features:
- Experiment tracking: Metrics, hyperparameters, system stats
- Sweeps: Hyperparameter search (grid, random, Bayesian)
- Artifacts: Dataset and model versioning
- Reports: Shareable documentation
Strengths: Beautiful visualizations, team collaboration, hyperparameter sweeps
Limitations: Cloud-dependent, limited self-hosting
Key concept: + - simple API, powerful features.
wandb.init()wandb.log()What to Track
| Category | Examples |
|---|---|
| Hyperparameters | Learning rate, batch size, architecture |
| Metrics | Loss, accuracy, F1, per-epoch values |
| Artifacts | Model checkpoints, configs, datasets |
| System | GPU usage, memory, runtime |
| Code | Git commit, diff, requirements |
Model Registry Concepts
| Stage | Purpose |
|---|---|
| None | Just logged, not registered |
| Staging | Testing, validation |
| Production | Serving live traffic |
| Archived | Deprecated, kept for reference |
Decision Guide
| Scenario | Recommendation |
|---|---|
| Self-hosted requirement | MLflow |
| Team collaboration | W&B |
| Model registry focus | MLflow |
| Hyperparameter sweeps | W&B |
| Beautiful dashboards | W&B |
| Full MLOps pipeline | MLflow + deployment tools |
Resources
- MLflow: https://mlflow.org/docs/latest/
- W&B: https://docs.wandb.ai/