Promoting models between stages in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Model Promotion in MLOps Pipelines

What is the primary purpose of promoting a machine learning model from a staging environment to production in an MLOps pipeline?

A To test the model with new data before final deployment

B To archive the model for future reference

C To make the model available for real users and applications

D To retrain the model with additional datasets

💡 Hint

Think about what 'production' means in software and model deployment.

Promoting a model to production means making it live so real users or systems can use it for predictions.

💻 Command Output

intermediate

Output of Model Promotion Command

Given the command below to promote a model version in MLflow, what is the expected output?

mlflow models transition-stage --model-name my_model --version 3 --stage Production

A Model version 3 transitioned to stage Production successfully.

B Error: Model version 3 does not exist.

C Model version 3 is already in Production stage.

D Syntax error: missing required flags.

💡 Hint

Assume the model version exists and the command syntax is correct.

The command transitions the specified model version to the Production stage, confirming success.

❓ Configuration

advanced

Configuring Automated Model Promotion

Which YAML snippet correctly configures a CI/CD pipeline step to promote a model to Production only if tests pass?

A

steps:
  - name: Promote Model
    run: mlflow models transition-stage --model-name my_model --version ${{ env.MODEL_VERSION }} --stage Production
    when: always()

B

steps:
  - name: Promote Model
    run: mlflow models transition-stage --model-name my_model --version ${{ env.MODEL_VERSION }} --stage Production
    if: failure()

C

steps:
  - name: Promote Model
    if: cancelled()
    run: mlflow models transition-stage --model-name my_model --version ${{ env.MODEL_VERSION }} --stage Production

D

steps:
  - name: Promote Model
    if: success()
    run: mlflow models transition-stage --model-name my_model --version ${{ env.MODEL_VERSION }} --stage Production

💡 Hint

Promotion should happen only when previous steps succeed.

The 'if: success()' condition ensures the promotion runs only if prior steps passed.

❓ Troubleshoot

advanced

Troubleshooting Model Promotion Failure

You run a model promotion command but receive the error: "Permission denied: cannot transition model stage." What is the most likely cause?

A The user lacks permissions to change model stages.

B The model version does not exist in the registry.

C The model is already in the target stage.

D The command syntax is incorrect.

💡 Hint

Think about access rights and roles in model registries.

Permission errors usually mean the user does not have rights to perform the action.

🔀 Workflow

expert

Correct Sequence for Model Promotion Workflow

Arrange the following steps in the correct order for promoting a machine learning model from staging to production:

A 1,4,2,3

B 4,1,2,3

C 2,4,1,3

D 4,2,1,3

💡 Hint

Think about the logical order from training to promotion.

First train the model, then validate it, register it, and finally promote it to production.