Comparing experiment runs in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

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

To identify which model or configuration performs best by analyzing differences in metrics, parameters, and outputs across multiple runs.

beginner

Name two common metrics used when comparing experiment runs.

Accuracy and loss are two common metrics used to compare experiment runs and evaluate model performance.

intermediate

How can visualizing experiment runs help in comparison?

Visualizations like line charts or scatter plots make it easier to spot trends, differences, and outliers between runs quickly.

intermediate

What role do parameters play in comparing experiment runs?

Parameters define the settings of each run; comparing them helps understand how changes affect model results.

advanced

Why is it important to compare experiment runs systematically?

Systematic comparison ensures fair evaluation, reproducibility, and informed decisions about model improvements.

Which of the following is NOT typically compared between experiment runs?

A Model accuracy

B Training time

C Hyperparameters

D Color of the computer case

The color of the computer case is unrelated to experiment runs and does not affect model performance.

What does comparing loss values between runs help determine?

A How well the model fits the data

B The size of the dataset

C How fast the computer runs

D The number of experiment runs

Loss values indicate how well the model fits the data; lower loss usually means better fit.

Which tool is commonly used to visualize experiment run comparisons?

A Experiment tracking platforms like MLflow

B Spreadsheet software

C Text editors

D Email clients

Experiment tracking platforms like MLflow provide built-in visualization tools for comparing runs.

Why should parameters be recorded for each experiment run?

A To decorate the report

B To understand how changes affect results

C To increase file size

D To confuse team members

Recording parameters helps understand the impact of different settings on model performance.

What is a key benefit of systematic experiment run comparison?

A Ignoring poor results

B Random guessing of best model

C Ensuring reproducibility and informed decisions

D Skipping documentation

Systematic comparison supports reproducibility and helps make informed choices about model improvements.

Explain how comparing experiment runs helps improve machine learning models.

Think about how looking at different runs side-by-side can guide your choices.

Describe the steps you would take to compare two experiment runs effectively.

Consider what information you need and how to present it clearly.

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