# Experiment Tracking

> Systematic recording of model experiments, hyperparameters, metrics, and artifacts.

- **Category**: Data Science
- **Subcategory**: MLOps
- **Canonical URL**: https://designpattern.fyi/patterns/experiment-tracking/

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## Description
**Context**: Data scientists run many experiments with different configurations. Without systematic tracking, reproducing results and comparing models becomes impossible.


## Use Cases
ML teams running multiple experiments needing to track, compare, and reproduce model training runs.



## Implementation Example

```python
# Experiment Tracking Pattern class ExperimentTracker: def __init__(self): self.experiments = []
def log_experiment(self, params, metrics, artifacts): experiment = { "params": params, "metrics": metrics, "artifacts": artifacts, "timestamp": datetime.now() } self.experiments.append(experiment)
def compare_experiments(self): return sorted(self.experiments, key=lambda x: x["metrics"]["accuracy"], reverse=True)
tracker = ExperimentTracker() tracker.log_experiment( params={"learning_rate": 0.01, "epochs": 100}, metrics={"accuracy": 0.92, "loss": 0.08}, artifacts=["model.pkl"] )
```



## Trade-offs


### Advantages

- - Reproducible experiments

- - Easy comparison

- - Collaboration support

- - Historical analysis




### Considerations & Drawbacks

- - Additional infrastructure

- - Learning curve

- - Storage costs

- - Adoption overhead







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**Reference**: [Original Source](https://www.mlflow.org/)