Comparing experiment runs in MLOps - Mini Project: Build & Apply

Comparing Experiment Runs

📖 Scenario: You are working as a data scientist running machine learning experiments. Each experiment run produces results with metrics like accuracy and loss. You want to compare these runs to see which one performed best.

🎯 Goal: Build a simple Python script that stores experiment runs as dictionaries, sets a metric to compare, filters runs that meet a threshold, and prints the best run based on that metric.

📋 What You'll Learn

Create a dictionary called experiment_runs with three runs and their metrics

Create a variable called metric_to_compare set to the metric name to compare

Use a dictionary comprehension to filter runs with metric values above a threshold

Print the run with the highest value for the chosen metric

💡 Why This Matters

🌍 Real World

Data scientists often run many experiments and need to compare results quickly to choose the best model.

💼 Career

Knowing how to organize and compare experiment results is key for roles in machine learning operations (MLOps) and data science.

1

Create experiment runs data

Create a dictionary called experiment_runs with these exact entries: 'run1': {'accuracy': 0.82, 'loss': 0.35}, 'run2': {'accuracy': 0.88, 'loss': 0.30}, 'run3': {'accuracy': 0.79, 'loss': 0.40}

MLOps

# Create the experiment_runs dictionary with exact entries
# Your code here

Hint

Use a dictionary with keys as run names and values as dictionaries of metrics.

2

Set the metric to compare

Create a variable called metric_to_compare and set it to the string 'accuracy'

MLOps

experiment_runs = {
    'run1': {'accuracy': 0.82, 'loss': 0.35},
    'run2': {'accuracy': 0.88, 'loss': 0.30},
    'run3': {'accuracy': 0.79, 'loss': 0.40}
}
# Set metric_to_compare to 'accuracy'
# Your code here

Hint

Just assign the string 'accuracy' to the variable metric_to_compare.

3

Filter runs above threshold

Create a dictionary called filtered_runs using a dictionary comprehension that includes only runs from experiment_runs where the metric_to_compare value is greater than 0.80

MLOps

experiment_runs = {
    'run1': {'accuracy': 0.82, 'loss': 0.35},
    'run2': {'accuracy': 0.88, 'loss': 0.30},
    'run3': {'accuracy': 0.79, 'loss': 0.40}
}
metric_to_compare = 'accuracy'
# Create filtered_runs dictionary with runs having accuracy > 0.80
# Your code here

Hint

Use a dictionary comprehension with for run, metrics in experiment_runs.items() and filter with if metrics[metric_to_compare] > 0.80.

4

Print the best run

Print the run name and its metrics from filtered_runs that has the highest value for metric_to_compare. Use max() with a key function to find the best run.

MLOps

experiment_runs = {
    'run1': {'accuracy': 0.82, 'loss': 0.35},
    'run2': {'accuracy': 0.88, 'loss': 0.30},
    'run3': {'accuracy': 0.79, 'loss': 0.40}
}
metric_to_compare = 'accuracy'
filtered_runs = {run: metrics for run, metrics in experiment_runs.items() if metrics[metric_to_compare] > 0.80}
# Print the best run based on accuracy
# Your code here

Hint

Use max(filtered_runs, key=lambda run: filtered_runs[run][metric_to_compare]) to find the best run, then print it.

Practice

1.

What is the main purpose of comparing experiment runs in MLOps?

easy

A. To identify which model performs best by reviewing their results side by side

B. To delete old experiment runs to save space

C. To create new experiment runs automatically

D. To change the code of the model during training

Solution

1. Step 1: Understand experiment runs

   Experiment runs record model training results and metrics.

2. Step 2: Purpose of comparing runs

   Comparing runs helps see which model version performs better by looking at their results side by side.

3. Final Answer:

   To identify which model performs best by reviewing their results side by side -> Option A

4. Quick Check:

   Comparing runs = find best model [OK]

Hint: Comparing runs means checking results to pick the best model [OK]

Common Mistakes:

• Thinking comparing runs deletes data
• Confusing comparing with creating runs
• Believing comparing changes model code

2.

Which command syntax correctly compares two experiment runs with IDs run1 and run2 under experiment exp123?

mlflow experiments compare-runs --experiment-id exp123 --run-ids run1 run2

easy

A. mlflow compare runs --experiment exp123 --ids run1,run2

B. mlflow experiments compare-runs --experiment-id exp123 --run-ids run1 run2

C. mlflow compare-runs --experiment exp123 --run-ids run1 run2

D. mlflow experiments compare --experiment-id exp123 --runs run1 run2

Solution

1. Step 1: Check official command format

   The correct MLflow command uses 'mlflow experiments compare-runs' with '--experiment-id' and '--run-ids' flags.

2. Step 2: Match options to syntax

   mlflow experiments compare-runs --experiment-id exp123 --run-ids run1 run2 matches the correct syntax exactly with proper flags and parameters.

3. Final Answer:

   mlflow experiments compare-runs --experiment-id exp123 --run-ids run1 run2 -> Option B

4. Quick Check:

   Correct command syntax = mlflow experiments compare-runs --experiment-id exp123 --run-ids run1 run2 [OK]

Hint: Use 'mlflow experiments compare-runs' with correct flags [OK]

Common Mistakes:

• Using wrong flags like --runs instead of --run-ids
• Mixing command order or names
• Separating run IDs with commas instead of spaces

3.

Given two runs with metrics:
run1: accuracy=0.85, loss=0.35
run2: accuracy=0.88, loss=0.40
Which run is better if accuracy is the main metric?

medium

A. run1 because it has higher accuracy

B. run1 because it has lower loss

C. run2 because it has higher accuracy

D. run2 because it has lower loss

Solution

1. Step 1: Identify main metric

   The question states accuracy is the main metric to compare runs.

2. Step 2: Compare accuracy values

   run1 accuracy = 0.85, run2 accuracy = 0.88. Higher accuracy is better.

3. Final Answer:

   run2 because it has higher accuracy -> Option C

4. Quick Check:

   Main metric accuracy = higher is better [OK]

Hint: Focus on main metric value to pick best run [OK]

Common Mistakes:

• Choosing run with lower loss when accuracy is main metric
• Confusing higher and lower metric values
• Ignoring stated main metric

4.

What is wrong with this command to compare runs?
mlflow experiments compare-runs --experiment-id exp123 --run-ids run1,run2

medium

A. Command should be 'mlflow compare-runs' without 'experiments'

B. Experiment ID flag should be --experiment, not --experiment-id

C. Run IDs must be specified with --runs, not --run-ids

D. Run IDs should be separated by spaces, not commas

Solution

1. Step 1: Check run IDs format

   MLflow expects run IDs separated by spaces, not commas.

2. Step 2: Verify other flags

   --experiment-id and --run-ids are correct flags; command includes 'experiments' correctly.

3. Final Answer:

   Run IDs should be separated by spaces, not commas -> Option D

4. Quick Check:

   Run IDs separated by spaces [OK]

Hint: Separate run IDs with spaces, not commas [OK]

Common Mistakes:

• Using commas between run IDs
• Changing correct flags incorrectly
• Removing 'experiments' from command

5.

You want to compare three runs but only focus on the f1_score metric. Which command correctly filters to show only this metric?

mlflow experiments compare-runs --experiment-id exp456 --run-ids runA runB runC --metric-keys f1_score

hard

A. mlflow experiments compare-runs --experiment-id exp456 --run-ids runA runB runC --metric-keys f1_score

B. mlflow experiments compare-runs --experiment-id exp456 --run-ids runA runB runC --metrics f1_score

C. mlflow experiments compare-runs --experiment-id exp456 --run-ids runA runB runC --filter f1_score

D. mlflow experiments compare-runs --experiment-id exp456 --run-ids runA runB runC --metric-filter f1_score

Solution

1. Step 1: Identify correct flag for metric filtering

   The correct flag to filter metrics in MLflow compare-runs is '--metric-keys'.

2. Step 2: Match command with options

   mlflow experiments compare-runs --experiment-id exp456 --run-ids runA runB runC --metric-keys f1_score uses '--metric-keys' correctly with the metric name 'f1_score'.

3. Final Answer:

   mlflow experiments compare-runs --experiment-id exp456 --run-ids runA runB runC --metric-keys f1_score -> Option A

4. Quick Check:

   Use --metric-keys to focus on specific metric [OK]

Hint: Use --metric-keys flag to show only chosen metric [OK]

Common Mistakes:

• Using wrong flag like --metrics or --filter
• Misspelling flag names
• Omitting metric filter when needed