Why experiment tracking prevents wasted work in MLOps - See It in Action

Why Experiment Tracking Prevents Wasted Work

📖 Scenario: You are working on a machine learning project where you try different settings to improve your model. Without keeping track, you might forget which settings worked best or repeat the same tests. This wastes time and effort.

🎯 Goal: Build a simple experiment tracker using a Python dictionary to record experiment names and their accuracy scores. Then, find the best experiment to avoid repeating work.

📋 What You'll Learn

Create a dictionary to store experiment names and accuracy scores

Add a new experiment result to the dictionary

Find the experiment with the highest accuracy

Print the best experiment and its accuracy

💡 Why This Matters

🌍 Real World

Experiment tracking helps data scientists remember which tests they ran and which worked best, saving time and effort.

💼 Career

Knowing how to track experiments is important for machine learning engineers and data scientists to improve models efficiently.

1

Create the initial experiment results dictionary

Create a dictionary called experiment_results with these exact entries: 'exp1': 0.75, 'exp2': 0.82, 'exp3': 0.78

MLOps

# Create the experiment_results dictionary with given entries
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with keys and values.

2

Add a new experiment result

Add a new entry to experiment_results with key 'exp4' and value 0.85

MLOps

experiment_results = {'exp1': 0.75, 'exp2': 0.82, 'exp3': 0.78}
# Add 'exp4' with accuracy 0.85 to experiment_results
# Your code here

Need a hint?

Use square brackets [] to add a new key-value pair to the dictionary.

3

Find the best experiment

Use the max function with experiment_results.items() and a key argument to find the experiment with the highest accuracy. Store the result in best_experiment.

MLOps

experiment_results = {'exp1': 0.75, 'exp2': 0.82, 'exp3': 0.78}
experiment_results['exp4'] = 0.85
# Find the experiment with the highest accuracy and assign to best_experiment
# Your code here

Need a hint?

The max function can take a key argument to compare dictionary values.

4

Print the best experiment and accuracy

Write a print statement to display the text: Best experiment: {experiment_name} with accuracy {accuracy} using the best_experiment tuple.

MLOps

experiment_results = {'exp1': 0.75, 'exp2': 0.82, 'exp3': 0.78}
experiment_results['exp4'] = 0.85
best_experiment = max(experiment_results.items(), key=lambda item: item[1])
# Print the best experiment name and accuracy
# Your code here

Need a hint?

Use an f-string to format the output with best_experiment[0] and best_experiment[1].