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

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

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

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

Hint

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

Practice

1. Why is experiment tracking important in machine learning projects?

easy

A. It replaces the need for data preprocessing.

B. It saves your work and helps avoid losing progress.

C. It automatically improves model accuracy without effort.

D. It guarantees the best model will be found every time.

Solution

1. Step 1: Understand the role of experiment tracking

   Experiment tracking records details of each test so progress is saved and not lost.

2. Step 2: Identify what experiment tracking does not do

   It does not automatically improve accuracy or replace data preprocessing.

3. Final Answer:

   It saves your work and helps avoid losing progress. -> Option B

4. Quick Check:

   Experiment tracking = saves work [OK]

Hint: Remember: tracking saves progress to avoid lost work [OK]

Common Mistakes:

• Thinking tracking improves model automatically
• Confusing tracking with data cleaning
• Assuming tracking guarantees best model

2. Which of the following is the correct way to log an experiment run using a tracking tool like MLflow in Python?

easy

A. mlflow.record_param('learning_rate', 0.01)

B. mlflow.save_param('learning_rate', 0.01)

C. mlflow.log_param('learning_rate', 0.01)

D. mlflow.store_param('learning_rate', 0.01)

Solution

1. Step 1: Recall MLflow parameter logging syntax

   The correct function to log parameters is mlflow.log_param(key, value).

2. Step 2: Identify incorrect function names

   Functions like save_param, record_param, and store_param do not exist in MLflow API.

3. Final Answer:

   mlflow.log_param('learning_rate', 0.01) -> Option C

4. Quick Check:

   MLflow logs params with log_param() [OK]

Hint: Use log_param() to record parameters in MLflow [OK]

Common Mistakes:

• Using non-existent MLflow functions
• Confusing log_param with save or store
• Misspelling function names

3. Given the following experiment tracking code snippet, what will be the output of print(results)?

results = []
for lr in [0.01, 0.1]:
    mlflow.log_param('learning_rate', lr)
    accuracy = 0.8 if lr == 0.01 else 0.75
    mlflow.log_metric('accuracy', accuracy)
    results.append((lr, accuracy))
print(results)

medium

A. [(0.01, 0.8), (0.1, 0.75)]

B. [(0.01, 0.75), (0.1, 0.8)]

C. [(0.01, 0.8), (0.1, 0.8)]

D. [(0.01, 0.75), (0.1, 0.75)]

Solution

1. Step 1: Analyze the loop and accuracy assignment

   For learning_rate 0.01, accuracy is 0.8; for 0.1, accuracy is 0.75.

2. Step 2: Check the appended results list

   Each tuple (lr, accuracy) is appended, so results = [(0.01, 0.8), (0.1, 0.75)].

3. Final Answer:

   [(0.01, 0.8), (0.1, 0.75)] -> Option A

4. Quick Check:

   Accuracy matches learning rate condition [OK]

Hint: Match accuracy values to learning rates carefully [OK]

Common Mistakes:

• Swapping accuracy values for learning rates
• Ignoring the if-else condition
• Appending wrong tuple order

4. You wrote this code to log experiments but no data appears in your tracking UI. What is the likely error?

mlflow.log_param('batch_size', 32)
mlflow.end_run()

medium

A. mlflow.end_run() should be called before logging parameters.

B. You need to call mlflow.start_run() as a context manager or assign it.

C. mlflow.log_param() is not the correct function to log parameters.

D. You forgot to call mlflow.start_run() before logging.

Solution

1. Step 1: Understand MLflow run management

   MLflow requires an active run (started with start_run()) before logging parameters.

2. Step 2: Identify the issue in the code

   The code attempts to log_param without calling start_run() first, so no run is active and data isn't logged.

3. Final Answer:

   You forgot to call mlflow.start_run() before logging. -> Option D

4. Quick Check:

   start_run() before log_param [OK]

Hint: Always mlflow.start_run() before logging params [OK]

Common Mistakes:

• Forgetting to call mlflow.start_run()
• Logging without an active run
• Calling end_run() without starting a run

5. You ran multiple experiments with different hyperparameters but forgot to track them. Later, you want to avoid repeating failed tests. How does experiment tracking help prevent wasted work in this scenario?

hard

A. By saving all experiment details, it allows you to compare and skip failed tests.

B. By automatically fixing failed experiments and rerunning them.

C. By deleting failed experiments so you only see successful ones.

D. By running all experiments again to confirm results.

Solution

1. Step 1: Understand the benefit of experiment tracking

   Tracking saves parameters, metrics, and results of each experiment for review.

2. Step 2: Explain how tracking prevents wasted work

   By reviewing saved experiments, you can identify failed tests and avoid repeating them, saving time.

3. Final Answer:

   By saving all experiment details, it allows you to compare and skip failed tests. -> Option A

4. Quick Check:

   Tracking = avoid repeating failed tests [OK]

Hint: Track experiments to skip repeats of failed tests [OK]

Common Mistakes:

• Thinking tracking fixes failures automatically
• Assuming failed tests are deleted
• Rerunning all tests blindly