Deployment and MLOps · MLD-0391
Experiment tracking
Recording configurations, metrics, artifacts, and context for training or evaluation runs.
Original reference page · detailed expansion pending. Original definitions, examples, notes and citations are preserved below. New examples are illustrative, not benchmark results.
Original example
Compare reproducible experiments.
Original caveat
Logged metrics are only meaningful when evaluation conditions are comparable.
Context and aliases
Level: Intermediate. Stages: Deployment · Monitoring.
Tags: ML · DL · LLM · SLM · MLOps.
Aliases: None recorded.
Related concepts
Concepts linking here
Technology connections
- BoTorch — Bayesian optimization toolkit built on PyTorch.
- ClearML — Experiment tracking and ML workflow platform.
- DVC — Data and pipeline versioning linked to Git workflows.
- lakeFS — Version-control-like operations for object storage.
- MLflow — Experiment, artifact, model and AI lifecycle tooling.
- Optuna — Hyperparameter optimization library.
- Ray Tune — Distributed experiment and hyperparameter tuning library.
- TensorBoard — Visualization tools for model training logs and artifacts.
- Weights & Biases — Experiment tracking and model-development platform.
Original references
- Transformers glossary ↗ · Consulted
- Google Cloud MLOps architecture ↗ · Reference link
- scikit-learn User Guide ↗ · Consulted
- Original reference URL ↗