Summary  
This chapter covers implementing code to log experiment parameters, performance metrics, and output artifacts in a structured format and applying consistent versioning and archiving practices to ensure reproducibility.

General domain of usage  
Machine learning experiments

When working on machine learning projects, you often run many experiments to tune models and improve performance. Without a systematic approach to tracking what you tried, it becomes difficult to reproduce results or understand which changes led to improvements. **Experiment tracking** involves recording key details about each run, including **parameters** used, **performance metrics**, and any output **artifacts** such as model files or plots. By logging this information, you create a clear record that helps you compare experiments, share results with others, and ensure that you can reproduce your work later.

import json

# Define experiment parameters
params = {
    "learning_rate": 0.01,
    "batch_size": 32,
    "num_epochs": 10
}

# Simulate experiment results
metrics = {
    "accuracy": 0.92,
    "loss": 0.15
}

# Combine parameters and results into a single record
experiment_log = {
    "parameters": params,
    "metrics": metrics
}

# Save experiment log to a JSON file
with open("experiment_001.json", "w") as f:
    json.dump(experiment_log, f, indent=4)

# Print confirmation
print("Experiment logged successfully!")

Ensuring **reproducibility** in machine learning experiments means that you or someone else can run the same code with the same data and get the same results. To achieve this, you should:
- Record all hyperparameters, random seeds, and environment settings;
- Save the versions of libraries and dependencies used;
- Store copies of the exact training and test data used;
- Log code versions (such as git commit hashes) associated with each experiment;
- Archive all output artifacts, such as trained models and evaluation plots.

By consistently applying these strategies, you make it possible to revisit past experiments, debug issues, and collaborate effectively with others.

Which of the following are essential to track in order to make machine learning experiments reproducible?

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Experiment Tracking and Reproducibility