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

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

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]

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

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.

Practice

1. What is the main purpose of a CI/CD pipeline in machine learning projects?

easy

A. To manually update model parameters

B. To automate model training, testing, and deployment

C. To store large datasets only

D. To write machine learning algorithms from scratch

Solution

1. Step 1: Understand CI/CD pipeline role

   CI/CD pipelines automate repetitive tasks like training, testing, and deploying ML models.

2. Step 2: Compare options

   Only To automate model training, testing, and deployment describes automation of these tasks; others describe unrelated activities.

3. Final Answer:

   To automate model training, testing, and deployment -> Option B

4. Quick Check:

   CI/CD automates ML workflows = B [OK]

Hint: CI/CD means automating ML workflow steps [OK]

Common Mistakes:

• Thinking CI/CD is only for code storage
• Confusing manual updates with automation
• Assuming CI/CD writes ML algorithms

2. Which of the following is the correct YAML syntax snippet to trigger a CI/CD pipeline on every code push in GitHub Actions?

easy

A. on: [push]

B. trigger: push

C. when: code_push

D. event: commit

Solution

1. Step 1: Identify GitHub Actions trigger syntax

   GitHub Actions uses 'on:' keyword with event names like 'push' to trigger workflows.

2. Step 2: Validate options

   Only 'on: [push]' is valid YAML syntax for triggering on code push.

3. Final Answer:

   on: [push] -> Option A

4. Quick Check:

   GitHub Actions trigger = on: [push] [OK]

Hint: GitHub Actions uses 'on:' for triggers [OK]

Common Mistakes:

• Using 'trigger' instead of 'on'
• Wrong keywords like 'when' or 'event'
• Incorrect YAML list syntax

3. Given this simplified ML pipeline step in a CI/CD YAML file:

steps:
  - name: Train model
    run: python train.py --epochs 5
  - name: Test model
    run: python test.py
  - name: Deploy model
    run: python deploy.py

What will happen if train.py fails during execution?

medium

A. The pipeline stops and does not run test or deploy steps

B. The pipeline continues to test and deploy steps regardless

C. Only the deploy step is skipped, test runs

D. The pipeline retries the train step automatically

Solution

1. Step 1: Understand default CI/CD step behavior

   By default, if a step fails, the pipeline stops and next steps do not run.

2. Step 2: Apply to given steps

   If 'train.py' fails, 'test.py' and 'deploy.py' steps will not execute.

3. Final Answer:

   The pipeline stops and does not run test or deploy steps -> Option A

4. Quick Check:

   Step failure stops pipeline = A [OK]

Hint: Failed step stops pipeline unless configured otherwise [OK]

Common Mistakes:

• Assuming pipeline continues after failure
• Thinking only deploy step is skipped
• Believing automatic retries happen by default

4. You wrote this GitHub Actions step to deploy your ML model:

- name: Deploy model
  run: python deploy.py
  if: success()

But the deploy step runs even if the test step fails. What is the likely cause?

medium

A. The deploy.py script always succeeds regardless of tests

B. The 'if' condition should be 'if: failure()' to run on failure

C. GitHub Actions ignores 'if' conditions on run steps

D. The 'if: success()' condition is incorrectly indented

Solution

1. Step 1: Check YAML indentation rules

   In GitHub Actions, 'if:' must be at the same level as 'run:' to apply correctly.

2. Step 2: Analyze given snippet

   'if: success()' is indented under 'run:', so it is ignored and deploy runs always.

3. Final Answer:

   The 'if: success()' condition is incorrectly indented -> Option D

4. Quick Check:

   YAML indentation affects condition execution = D [OK]

Hint: Check YAML indentation for 'if:' conditions [OK]

Common Mistakes:

• Misplacing 'if:' under 'run:'
• Confusing success() with failure()
• Assuming scripts control step execution

5. You want to automate retraining your ML model weekly and also on every code push. Which CI/CD configuration best achieves this?

hard

A. trigger: push\nschedule: weekly\ncron: '0 0 * * 0'

B. on: push\nschedule: 'weekly' # invalid syntax

C. on: [push, schedule]\nschedule:\n - cron: '0 0 * * 0' # every Sunday midnight

D. on: [push]\nschedule: every Sunday

Solution

1. Step 1: Understand GitHub Actions triggers

   To trigger on code push and schedule, use 'on:' with both 'push' and 'schedule' keys.

2. Step 2: Validate cron syntax

   on: [push, schedule]\nschedule:\n - cron: '0 0 * * 0' # every Sunday midnight uses correct cron format for weekly schedule and valid YAML syntax.

3. Final Answer:

   on: [push, schedule]\nschedule:\n - cron: '0 0 * * 0' # every Sunday midnight -> Option C

4. Quick Check:

   Use 'on:' with push and schedule + valid cron [OK]

Hint: Use 'on:' with push and schedule plus cron for weekly [OK]

Common Mistakes:

• Using invalid schedule syntax
• Misplacing cron outside schedule
• Using unsupported keywords like 'trigger'