Automated model validation before promotion in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to run model validation using a CI pipeline step.

MLOps

steps:
  - name: Validate Model
    run: python validate_model.py --model [1]

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

Alatest_model.pkl

Btrain.py

Cmodel.pkl

Ddeploy.sh

Attempts:

3 left

💡 Hint

Common Mistakes

Using a script file instead of the model file.

Using a deployment script instead of the model.

The validation script requires the model file, which is typically named model.pkl.

2fill in blank

medium

Complete the code to check if model accuracy meets the threshold before promotion.

MLOps

if accuracy [1] 0.85:
    print('Model passed validation')
else:
    print('Model failed validation')

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

A!=

B<

C==

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than operator which rejects good models.

Using equality which is too strict.

The model should pass validation if accuracy is greater than or equal to 0.85.

3fill in blank

hard

Fix the error in the validation script to correctly load the model file.

MLOps

with open('[1]', 'rb') as f:
    model = pickle.load(f)

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

Amodel.pkl

Bmodel.json

Cmodel.txt

Dmodel.py

Attempts:

3 left

💡 Hint

Common Mistakes

Using a text or JSON file which causes load errors.

Using a Python script file instead of a model file.

The model file is a binary pickle file, so it should be model.pkl.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters models with accuracy above 0.9.

MLOps

high_accuracy_models = {model[1]: acc for model, acc in results.items() if acc [2] 0.9}

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

A.upper()

B>

C>=

D.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase instead of uppercase for model names.

Using >= instead of > which includes 0.9 exactly.

We convert model names to uppercase and select those with accuracy greater than 0.9.

5fill in blank

hard

Fill all three blanks to create a dictionary of models with accuracy above 0.8 and keys as lowercase model names.

MLOps

validated_models = { [1]: [2] for [3] in results.items() if [2] > 0.8}

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

Amodel.lower()

Bacc

C(model, acc)

Daccuracy

Attempts:

3 left

💡 Hint

Common Mistakes

Incorrect iteration variable like 'model' instead of '(model, acc)'.

Using wrong variable names for iteration or values.

The dictionary keys are lowercase model names, values are accuracies, iterating over model, acc pairs.