Why Comparing experiment runs in MLOps? - Purpose & Use Cases

The Big Idea

What if you could instantly know which experiment is best without digging through messy notes?

The Scenario

Imagine you have run several machine learning experiments manually, each with different settings. You write down results on paper or in separate files and try to remember which settings gave the best outcome.

The Problem

This manual tracking is slow and confusing. You might mix up results, forget details, or spend hours comparing numbers by hand. It's easy to make mistakes and miss the best experiment.

The Solution

Comparing experiment runs with tools lets you automatically track all settings and results in one place. You can quickly see differences side-by-side and find the best model without guesswork.

Before vs After

✗ Before

Run experiment A, save results in file A.txt
Run experiment B, save results in file B.txt
Open both files and compare manually

✓ After

mlflow run experiment_A
mlflow run experiment_B
mlflow ui to compare runs side-by-side

What It Enables

You can easily identify the best experiment and improve your models faster with clear, automatic comparisons.

Real Life Example

A data scientist runs 10 versions of a model with different parameters. Using experiment comparison, they instantly see which version performs best and why, saving days of manual work.

Key Takeaways

Manual tracking of experiments is slow and error-prone.

Automated comparison tools organize and display results clearly.

This speeds up finding the best model and improves productivity.

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