CI/CD for ML pipelines in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding CI/CD stages in ML pipelines

Which stage in a CI/CD pipeline for ML is responsible for automatically retraining the model when new data arrives?

A Model deployment stage

B Continuous training stage

C Continuous integration stage

D Monitoring stage

💡 Hint

Think about which stage updates the model with fresh data.

The continuous training stage automatically retrains the ML model when new data is available, ensuring the model stays accurate over time.

💻 Command Output

intermediate

Output of a pipeline status command

What is the output of the command mlflow pipelines status --pipeline-name churn-prediction if the pipeline is currently running?

ML Python

mlflow pipelines status --pipeline-name churn-prediction

A Pipeline 'churn-prediction' status: RUNNING

B Error: Pipeline not found

C Pipeline 'churn-prediction' status: FAILED

D No pipelines currently running

💡 Hint

Look for the status message indicating the pipeline is active.

The command shows the current status of the specified pipeline. If it is running, the output will confirm that.

❓ Configuration

advanced

Correct YAML snippet for triggering retraining

Which YAML snippet correctly configures a GitHub Actions workflow to trigger model retraining on every push to the main branch?

ML Python

name: ML Retrain
on:
  push:
    branches:
      - main
jobs:
  retrain:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Run retrain script
        run: python retrain.py

A

on:
  schedule:
    - cron: '0 0 * * *'

B

on:
  pull_request:
    branches:
      - main

C

on:
  push:
    branches:
      - main

D

on:
  push:
    tags:
      - main

💡 Hint

Look for the event that triggers on code push to the main branch.

Option C correctly triggers the workflow on every push to the main branch. Other options trigger on pull requests, tags, or scheduled times.

❓ Troubleshoot

advanced

Diagnosing failed model deployment

You have a CI/CD pipeline that builds and deploys an ML model. The build succeeds but deployment fails with the error: Permission denied: unable to push to model registry. What is the most likely cause?

A The model artifact is missing from the build

B The deployment script has a syntax error

C The model registry service is down

D The CI/CD pipeline lacks permissions to write to the model registry

💡 Hint

Consider access rights needed for deployment.

The error indicates the pipeline cannot push the model due to missing permissions. Fixing access rights resolves this.

🔀 Workflow

expert

Order of steps in a CI/CD ML pipeline

Arrange the following steps in the correct order for a typical CI/CD pipeline for ML models:

1. Deploy model to production
2. Run automated tests on model code
3. Train model with new data
4. Build model artifact

A 1,2,3,4

B 2,1,3,4

C 1,3,2,4

D 3,2,1,4

💡 Hint

Think about testing before training, then building, then deploying.

The correct order is to first run tests on code, then train the model, build the artifact, and finally deploy it.

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'