Model stages (staging, production, archived) in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Model Stages Purpose

Which statement best describes the purpose of the staging stage in a model lifecycle?

A It is where the model is tested in an environment similar to production before full deployment.

B It is the final stage where the model is actively used to make predictions for users.

C It is where old models are stored and no longer used for predictions.

D It is the initial phase where the model is being trained and developed.

💡 Hint

Think about the stage that acts as a 'practice run' before the model goes live.

The staging stage is used to test the model in an environment that mimics production. This helps catch issues before the model is fully deployed.

💻 Command Output

intermediate

Identifying Model Stage from CLI Output

You run a command to list models and their stages. What is the stage of the model named sales_forecast_v2?

MLOps

Model Name          Stage
sales_forecast_v1   archived
sales_forecast_v2   production
sales_forecast_v3   staging

A staging

B archived

C production

D training

💡 Hint

Look at the row with the model name sales_forecast_v2.

The output shows sales_forecast_v2 is in the production stage, meaning it is actively used for predictions.

🔀 Workflow

advanced

Correct Workflow for Promoting a Model to Production

Which sequence correctly describes the workflow to promote a model from staging to production?

A 1,2,3,4

B 2,1,4,3

C 1,3,2,4

D 3,1,2,4

💡 Hint

Think about testing before deploying and cleaning up old models after deployment.

The correct workflow is to test in staging, deploy to production, archive old models, then monitor performance.

❓ Troubleshoot

advanced

Troubleshooting Model Stage Mislabeling

You notice a model labeled as production is actually outdated and should not serve predictions. What is the best immediate action?

A Ignore the label and continue using the model.

B Delete the model files from storage immediately.

C Retrain the model and keep the stage as production.

D Change the model stage to <code>archived</code> to prevent it from being used.

💡 Hint

Think about how to stop the model from being used without losing data.

Changing the stage to archived prevents the model from being used while keeping it for records or rollback.

✅ Best Practice

expert

Best Practice for Managing Archived Models

What is the best practice for handling archived models in a production system?

A Use archived models as the default for new predictions.

B Keep archived models in a separate storage with restricted access for audit and rollback purposes.

C Move archived models back to staging for retraining without review.

D Delete archived models immediately to save storage space.

💡 Hint

Consider the importance of old models for audits and recovery.

Archived models should be stored safely with limited access to allow audits and rollback if needed.

Practice

1. What is the primary purpose of the staging stage in model lifecycle management?

easy

A. To deploy models for live user traffic

B. To test and validate models before they go live

C. To permanently delete old models

D. To archive models for long-term storage

Solution

1. Step 1: Understand model stages

   The staging stage is used to test and validate models before they are deployed to production.

2. Step 2: Differentiate from other stages

   Production is for live use, archived is for storage, so staging is the testing phase.

3. Final Answer:

   To test and validate models before they go live -> Option B

4. Quick Check:

   Staging = Testing phase [OK]

Hint: Staging means testing before live use [OK]

Common Mistakes:

• Confusing staging with production
• Thinking archived means active use
• Assuming staging is for deleting models

2. Which MLflow command correctly transitions a model version to the production stage?

easy

A. mlflow models transition-version --model-name my_model --version 3 --stage production

B. mlflow model transition-version --model-name my_model --version 3 --to-stage production

C. mlflow models transition-version --model-name my_model --model-version 3 --stage production

D. mlflow models transition --model-name my_model --model-version 3 --stage production

Solution

1. Step 1: Recall MLflow CLI syntax

   The correct MLflow CLI command to transition a model version is mlflow models transition-version with flags --model-name, --version and --stage.

2. Step 2: Identify correct flags and command

   mlflow models transition-version --model-name my_model --version 3 --stage production matches the correct syntax.

3. Final Answer:

   mlflow models transition-version --model-name my_model --version 3 --stage production -> Option A

4. Quick Check:

   Correct MLflow syntax = mlflow models transition-version --model-name my_model --version 3 --stage production [OK]

Hint: Use 'transition-version' with --version and --stage flags [OK]

Common Mistakes:

• Using 'model' instead of 'models'
• Mixing up --model-version and --version flags
• Incorrect command order or missing flags

3. Given this MLflow Python snippet, what will be the stage of the model after execution?

from mlflow import MlflowClient
client = MlflowClient()
client.transition_model_version_stage(
    name="my_model",
    version=2,
    stage="archived"
)

medium

A. Archived

B. Production

C. Staging

D. None (error)

Solution

1. Step 1: Understand the method call

   The method transition_model_version_stage changes the stage of a model version to the specified stage.

2. Step 2: Check the stage argument

   The stage argument is set to "archived", so the model version 2 of "my_model" will be moved to archived stage.

3. Final Answer:

   Archived -> Option A

4. Quick Check:

   transition_model_version_stage with stage='archived' = Archived [OK]

Hint: Stage argument sets the model's new stage directly [OK]

Common Mistakes:

• Assuming default stage if not specified
• Confusing method name with other MLflow functions
• Thinking 'archived' means deletion

4. You run this MLflow CLI command but get an error:

mlflow models transition-version --version 5 --stage production

What is the most likely cause?

medium

A. The command should be mlflow model transition-version (singular 'model')

B. Incorrect stage name; should be 'prod' instead of 'production'

C. Version number must be a string, not a number

D. Missing the --model-name argument

Solution

1. Step 1: Check required arguments for transition-version

   The mlflow models transition-version command requires the --model-name argument to specify which model to update.

2. Step 2: Identify missing argument

   The command lacks --model-name, causing the error.

3. Final Answer:

   Missing the --model-name argument -> Option D

4. Quick Check:

   Missing required flags cause errors [OK]

Hint: Always include --model-name when transitioning versions [OK]

Common Mistakes:

• Assuming stage names are abbreviated
• Using singular 'model' instead of 'models'
• Passing version as string instead of number (both accepted)

5. You have two model versions: v1 in production and v2 in staging. You want to promote v2 to production and archive v1 using MLflow Python API. Which sequence of calls correctly achieves this?

hard

A. client.transition_model_version_stage('model', 2, 'archived') then client.transition_model_version_stage('model', 1, 'production')

B. client.transition_model_version_stage('model', 2, 'production') then client.transition_model_version_stage('model', 2, 'archived')

C. client.transition_model_version_stage('model', 1, 'archived') then client.transition_model_version_stage('model', 2, 'production')

D. client.transition_model_version_stage('model', 1, 'production') then client.transition_model_version_stage('model', 2, 'staging')

Solution

1. Step 1: Archive current production version first

   To free the production stage, first move v1 from production to archived.

2. Step 2: Promote staging version to production

   After archiving v1, promote v2 from staging to production.

3. Final Answer:

   Archive v1 then promote v2 -> Option C

4. Quick Check:

   Archive old before promoting new [OK]

Hint: Archive old production before promoting new [OK]

Common Mistakes:

• Promoting new before archiving old causes stage conflict
• Archiving staging version instead of production
• Reassigning stages incorrectly