Model versioning in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to save a trained model using versioning.

ML Python

model.save('model_v[1].h5')

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A1

Bbest

Cfinal

Dlatest

Attempts:

3 left

💡 Hint

Common Mistakes

Using strings like 'latest' or 'best' without a number can cause confusion in versioning.

The version number '1' is used to save the first version of the model file.

2fill in blank

medium

Complete the code to load a specific model version.

ML Python

loaded_model = load_model('model_v[1].h5')

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A2

Blatest

Cfinal

Dbest

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-numeric strings like 'best' or 'latest' without a clear version number.

Loading model version '2' specifies which saved model to use.

3fill in blank

hard

Fix the error in the code to properly save the model with versioning.

ML Python

model.save('model_[1].h5')

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A1v

Bversion1

Cv1

Dver1

Attempts:

3 left

💡 Hint

Common Mistakes

Using '1v' or 'ver1' which are less clear or unconventional.

The convention 'v1' clearly indicates version 1 and is commonly used.

4fill in blank

hard

Fill both blanks to create a dictionary that maps model versions to their file paths.

ML Python

model_versions = {'v1': '[1]', 'v2': '[2]'}

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Amodel_v1.h5

Bmodel_latest.h5

Cmodel_v2.h5

Dmodel_best.h5

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing version keys with unrelated file names like 'latest' or 'best'.

The dictionary keys 'v1' and 'v2' map to their respective model files.

5fill in blank

hard

Fill all three blanks to load a model version from the dictionary and make a prediction.

ML Python

model_path = model_versions['[1]']
loaded_model = load_model(model_path)
prediction = loaded_model.predict([2])
print('Prediction for version [3]:', prediction)

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

Av2

Binput_data

Cv1

Dtest_data

Attempts:

3 left

💡 Hint

Common Mistakes

Using mismatched version keys or wrong data variable names.

We load version 'v1', predict on 'test_data', and print the prediction for 'v1'.