CI/CD for ML pipelines in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to trigger a pipeline run on every push to the main branch.

ML Python

on:
  push:
    branches:
      - [1]

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

Adevelop

Bfeature

Cmain

Drelease

Attempts:

3 left

💡 Hint

Common Mistakes

Using a feature branch name instead of the main branch.

The main branch is the default branch where we want to trigger the pipeline on every push.

2fill in blank

medium

Complete the code to specify the job that runs the ML training script.

ML Python

jobs:
  train_model:
    runs-on: ubuntu-latest
    steps:
      - name: Run training
        run: python [1]

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

Atest.py

Btrain.py

Cdeploy.py

Devaluate.py

Attempts:

3 left

💡 Hint

Common Mistakes

Using evaluation or deployment scripts instead of training script.

The training script is usually named train.py to run the ML training process.

3fill in blank

hard

Fix the error in the pipeline step that installs dependencies.

ML Python

steps:
  - name: Install dependencies
    run: pip [1] -r requirements.txt

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Ainstall

Bdownload

Cupdate

Dupgrade

Attempts:

3 left

💡 Hint

Common Mistakes

Using pip download or pip update which do not install packages.

The correct pip command to install dependencies from a requirements file is pip install -r requirements.txt.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps dataset names to their sizes if size is greater than 1000.

ML Python

dataset_sizes = {name: size for name, size in datasets.items() if size [1] [2]

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A>

B1000

C<

D500

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than operator or wrong threshold values.

The comprehension filters datasets with size greater than 1000 using size > 1000.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps model names in uppercase to their accuracy if accuracy is above 0.8.

ML Python

filtered_models = { [1]: [2] for model, accuracy in models.items() if accuracy [3] 0.8 }

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

Amodel.upper()

Baccuracy

C>

Dmodel.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase conversion or wrong comparison operators.

The comprehension maps uppercase model names to their accuracy if accuracy is greater than 0.8.