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Data Science
MLOps
Experiment Tracking
Systematic recording of model experiments, hyperparameters, metrics, and artifacts.
Intent & Description
📋 Context
Data scientists run many experiments with different configurations. Without systematic tracking, reproducing results and comparing models becomes impossible.
Real-world Use Case
ML teams running multiple experiments needing to track, compare, and reproduce model training runs.
Source
Advantages
- Reproducible experiments
- Easy comparison
- Collaboration support
- Historical analysis
Disadvantages
- Additional infrastructure
- Learning curve
- Storage costs
- Adoption overhead
Implementation Example
# 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"] )