Why platforms accelerate ML team productivity in MLOps - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to start a new ML experiment using the platform's API.

MLOps

experiment = ml_platform.[1]('my_experiment')

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Ainitiate

Bstart_experiment

Ccreate_experiment

Drun

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'run' or 'initiate' which do not exist in the API.

Confusing 'start_experiment' with the actual method name.

The create_experiment method initializes a new experiment on the ML platform, which is the correct way to start tracking.

2fill in blank

medium

Complete the code to log a metric value to the ML platform.

MLOps

experiment.log_metric('[1]', 0.85)

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Aaccuracy

Bscore_accuracy

Caccuracy_score

Dmetric_accuracy

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-standard metric names like 'accuracy_score' which may not be recognized.

Adding prefixes or suffixes unnecessarily.

The standard metric name used is accuracy. This is the key expected by the platform to log accuracy values.

3fill in blank

hard

Fix the error in the code to properly save the trained model artifact.

MLOps

experiment.[1]_artifact('model.pkl')

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Alog

Bstore

Csave

Dupload

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'save_artifact' which is not a valid method.

Using 'upload_artifact' which may not exist in the API.

The correct method to save an artifact in the ML platform is log_artifact. This tracks the file with the experiment.

4fill in blank

hard

Fill both blanks to filter experiments by status and sort by creation date.

MLOps

experiments = sorted(ml_platform.get_experiments(status=[1]), key=lambda x: x.[2])

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A'completed'

B'failed'

Ccreation_time

Dstart_time

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'failed' status which filters out successful experiments.

Sorting by 'start_time' which may not reflect creation order.

Filtering by 'completed' status shows finished experiments. Sorting by creation_time orders them by when they were created.

5fill in blank

hard

Fill all three blanks to create a dictionary of model names to their accuracy if accuracy is above 0.8.

MLOps

high_accuracy_models = {model[1]: metrics[2] for model, metrics in model_results.items() if metrics[2][3] 0.8}

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A.upper()

B['accuracy']

C>

D.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using '.lower()' instead of '.upper()' for model names.

Comparing metrics directly instead of metrics['accuracy'].

The model names are converted to uppercase with .upper(). The accuracy metric is accessed with ['accuracy']. The filter keeps models with accuracy greater than 0.8.