experiment-tracking
Use when "experiment tracking", "MLflow", "Weights & Biases", "wandb", "model registry", "hyperparameter logging", "ML experiments", "training metrics"
How do I install this agent skill?
npx skills add https://github.com/eyadsibai/ltk --skill experiment-trackingIs this agent skill safe to install?
- Gen Agent Trust Hubpass
This skill is an informational guide providing comparisons and reference material for machine learning experiment tracking tools. It contains no executable code, scripts, or instructions that pose a security risk.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
- Runlayerwarn
1/1 file flagged
What does this agent skill do?
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: wandb.init() + wandb.log() - simple API, powerful features.
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/
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/eyadsibai/ltk/experiment-tracking">View experiment-tracking on skillZs</a>