Logging parameters and metrics in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to log a parameter using MLflow.

MLOps

mlflow.log_param("learning_rate", [1])

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

A0.01

Blearning_rate

C"0.01"

Dparam

Attempts:

3 left

💡 Hint

Common Mistakes

Putting the number inside quotes makes it a string, which is allowed but not typical for numeric parameters.

Using variable names without defining them causes errors.

The log_param function expects the parameter value, here a float 0.01 is correct.

2fill in blank

medium

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

MLOps

mlflow.log_metric("accuracy", [1])

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

A0.95

Bmetric

Caccuracy

D"0.95"

Attempts:

3 left

💡 Hint

Common Mistakes

Using strings instead of numbers for metric values.

Confusing metric names with values.

The log_metric function expects a numeric value for the metric, so 0.95 is correct.

3fill in blank

hard

Fix the error in logging a parameter where the variable 'lr' holds the learning rate.

MLOps

mlflow.log_param("learning_rate", [1])

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

Alearning_rate

Blr

C"lr"

DlearningRate

Attempts:

3 left

💡 Hint

Common Mistakes

Passing variable names as strings instead of variables.

Using undefined variable names.

The variable lr holds the value to log, so it should be passed directly without quotes.

4fill in blank

hard

Fill both blanks to log a metric 'f1_score' with value stored in variable 'score'.

MLOps

mlflow.log_metric([1], [2])

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

A"f1_score"

Bscore

Cf1_score

D"score"

Attempts:

3 left

💡 Hint

Common Mistakes

Using variable names without quotes for metric names.

Putting variable names inside quotes for values.

The metric name must be a string, so "f1_score" is correct. The value is the variable score.

5fill in blank

hard

Fill all three blanks to log parameters and metrics in a run.

MLOps

with mlflow.start_run():
    mlflow.log_param("batch_size", [1])
    mlflow.log_metric("accuracy", [2])
    mlflow.log_metric("loss", [3])

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

A32

B0.89

C0.12

Dbatch_size

Attempts:

3 left

💡 Hint

Common Mistakes

Using variable names instead of values.

Putting numeric values inside quotes.

Parameters and metrics are logged with their values: batch size as integer 32, accuracy and loss as floats 0.89 and 0.12 respectively.