Comparing experiment runs in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the experiment run by its ID.

MLOps

run = client.get_run([1])

Drag options to blanks, or click blank then click option'

Aexperiment_id

Brun_id

Cproject_name

Duser_name

Attempts:

3 left

💡 Hint

Common Mistakes

Using experiment_id instead of run_id

Passing project_name which is not accepted by get_run

The method get_run requires the run ID to fetch the specific experiment run.

2fill in blank

medium

Complete the code to compare two runs by their metrics.

MLOps

metrics_diff = run1.data.metrics[[1]] - run2.data.metrics[[1]]

Drag options to blanks, or click blank then click option'

A'user_id'

B'run_id'

C'start_time'

D'accuracy'

Attempts:

3 left

💡 Hint

Common Mistakes

Using run_id which is not a metric

Using start_time which is a timestamp, not a metric

Metrics like 'accuracy' are stored in the metrics dictionary and can be compared between runs.

3fill in blank

hard

Fix the error in the code to fetch the latest run of an experiment.

MLOps

latest_run = client.search_runs(experiment_ids=[[1]], order_by=['start_time DESC'])[0]

Drag options to blanks, or click blank then click option'

Aexperiment_id

Brun_id

Cproject_id

Duser_id

Attempts:

3 left

💡 Hint

Common Mistakes

Passing run_id instead of experiment_id

Using project_id which is not accepted here

The search_runs method requires experiment_ids to filter runs by experiment.

4fill in blank

hard

Fill both blanks to create a dictionary of metric differences between two runs.

MLOps

diffs = {metric: run1.data.metrics[[1]] - run2.data.metrics[[1]] for metric in run1.data.metrics if metric [2] run2.data.metrics}

Drag options to blanks, or click blank then click option'

Ametric

B==

C!=

Din

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of 'in' to check dictionary keys

Using a wrong variable name instead of 'metric'

We use 'metric' as the key to access values and check if the metric exists in both runs using 'in'.

5fill in blank

hard

Fill all three blanks to filter runs with metric improvement and create a summary dictionary.

MLOps

summary = {run.data.metrics[[1]]: run.data.metrics[[2]] for run in runs if run.data.metrics[[3]] > 0.8}

Drag options to blanks, or click blank then click option'

A'accuracy'

B'loss'

C'precision'

D'recall'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same metric for all blanks without considering filtering condition

Using metrics that may not exist in all runs

We use 'accuracy' as the key and 'loss' as the value in the summary, filtering runs where 'accuracy' is greater than 0.8.

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