Performance metric tracking in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to log a metric named 'accuracy' with value 0.95 using MLflow.

MLOps

mlflow.log_metric('[1]', 0.95)

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

Aloss

Baccuracy

Cprecision

Drecall

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong metric name like 'loss' or 'precision'.

The metric name should be 'accuracy' to correctly log the accuracy value.

2fill in blank

medium

Complete the code to start an MLflow run before logging metrics.

MLOps

with mlflow.[1]():
    mlflow.log_metric('accuracy', 0.95)

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

Astart_run

Brun_start

Cbegin_run

Dopen_run

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect method names like 'run_start' or 'begin_run'.

The correct method to start an MLflow run is start_run().

3fill in blank

hard

Fix the error in the code to log the metric 'loss' with value 0.1 inside an MLflow run.

MLOps

with mlflow.start_run():
    mlflow.log_metric('loss', [1])

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

A0.1

B'0.1'

Closs

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Putting the number in quotes, which makes it a string.

The metric value should be a number, so use 0.1 without quotes.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that tracks metrics only if their value is greater than 0.5.

MLOps

metrics = {k: v for k, v in all_metrics.items() if v [1] [2]

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A>

B0.5

C<

D1.0

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' or wrong threshold values.

The condition should check if the metric value is greater than 0.5 using v > 0.5.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of metrics where keys are uppercase and values are above 0.7.

MLOps

filtered_metrics = [1]: v for k, v in metrics.items() if v [2] [3]

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Ak.upper()

B>

C0.7

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys or wrong comparison operators.

The keys are converted to uppercase with k.upper(), and values filtered with v > 0.7.