Promoting models between stages in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to promote a model to the 'Staging' stage using MLflow.

MLOps

mlflow_client.transition_model_version_stage(name='my_model', version=[1], stage='Staging')

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

A'production'

B'latest'

C0

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using stage names instead of version number

Using strings like 'latest' instead of version number

The version parameter expects the model version number as an integer or string. Here, 1 is the correct version number to promote.

2fill in blank

medium

Complete the code to check the current stage of a model version.

MLOps

model_version = mlflow_client.get_model_version(name='my_model', version=[1])
current_stage = model_version.[2]

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

A1

Bstage

Cstatus

Dversion

Attempts:

3 left

💡 Hint

Common Mistakes

Using attribute names instead of version number

Using stage names instead of version number

The version parameter must be the model version number, here 1. The current stage is accessed via model_version.stage.

3fill in blank

hard

Fix the error in the code to promote a model version to 'Production' stage.

MLOps

mlflow_client.transition_model_version_stage(name='my_model', version=2, stage=[1])

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

A'Production'

B'staging'

C'production'

Dproduction

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase 'production'

Using unquoted stage names

The stage name must be a title case string exactly matching MLflow's expected stage names. Use 'Production' with capital 'P'.

4fill in blank

hard

Fill both blanks to create a dictionary mapping model versions to their stages.

MLOps

version_stage_map = {mv.version: mv.[1] for mv in mlflow_client.search_model_versions('name="my_model"') if mv.[2] != 'Archived'}

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

Astage

Bstatus

Dversion

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'status' instead of 'stage'

Using 'version' instead of 'stage'

The attribute stage holds the stage name of the model version. We use it both to get the stage and to filter out 'Archived' versions.

5fill in blank

hard

Fill all three blanks to promote all 'Staging' models to 'Production' stage.

MLOps

for mv in mlflow_client.search_model_versions('name="my_model"'):
    if mv.[1] == '[2]':
        mlflow_client.transition_model_version_stage(name='my_model', version=mv.[3], stage='Production')

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

Astage

BStaging

Cversion

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'status' instead of 'stage'

Using 'stage' as version

Using unquoted stage names

We check the stage attribute to find models in 'Staging'. Then we use the version attribute to promote each model to 'Production'.

Practice

1. What is the main purpose of promoting a machine learning model between stages like testing and production?

easy

A. To move a model to a more stable and reliable environment

B. To delete old versions of the model

C. To retrain the model with new data automatically

D. To change the model's algorithm

Solution

1. Step 1: Understand model promotion

   Promoting a model means moving it from one stage to another, like from testing to production.

2. Step 2: Identify the purpose of promotion

   This process ensures the model is stable and reliable before it is used live.

3. Final Answer:

   To move a model to a more stable and reliable environment -> Option A

4. Quick Check:

   Model promotion = move to stable environment [OK]

Hint: Promotion means moving model to a safer, stable stage [OK]

Common Mistakes:

• Confusing promotion with retraining
• Thinking promotion deletes models
• Assuming promotion changes model algorithms

2. Which of the following is the correct command syntax to promote a model named my_model version 3 to the Production stage using MLflow CLI?

easy

A. mlflow models promote --name my_model --version 3 --stage Production

B. mlflow model promote --model my_model --ver 3 --to Production

C. mlflow models transition --name my_model --version 3 --stage Production

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

Solution

1. Step 1: Identify correct MLflow CLI command

   The MLflow CLI uses mlflow models transition-stage to promote models between stages.

2. Step 2: Check command options

   The correct options are --model-name, --version, and --stage to specify the model, version, and target stage.

3. Final Answer:

   mlflow models transition-stage --model-name my_model --version 3 --stage Production -> Option D

4. Quick Check:

   MLflow promote command = transition-stage with correct flags [OK]

Hint: Use 'transition-stage' with --model-name, --version, --stage flags [OK]

Common Mistakes:

• Using 'promote' instead of 'transition-stage'
• Wrong flag names like --name or --ver
• Mixing singular/plural 'model' vs 'models'

3. Given the following MLflow CLI command:
mlflow models transition-stage --model-name sales_forecast --version 5 --stage Staging
What will be the result of running this command?

medium

A. The model version 5 of sales_forecast is moved to the Staging stage

B. The model sales_forecast version 5 is deleted

C. A new version 6 of sales_forecast is created in Staging

D. The model sales_forecast version 5 is retrained automatically

Solution

1. Step 1: Understand the command purpose

   The command transition-stage moves a specific model version to a new stage.

2. Step 2: Analyze the command parameters

   It targets model sales_forecast, version 5, moving it to Staging stage.

3. Final Answer:

   The model version 5 of sales_forecast is moved to the Staging stage -> Option A

4. Quick Check:

   transition-stage moves model version to new stage [OK]

Hint: transition-stage moves specified version to target stage [OK]

Common Mistakes:

• Thinking it deletes or retrains the model
• Assuming it creates a new version
• Confusing model name and version

4. You run the command mlflow models transition-stage --model-name my_model --version 2 --stage Production but get an error saying "Stage 'Production' does not exist." What is the most likely cause and fix?

medium

A. The stage name is case-sensitive; change 'Production' to 'production'

B. The stage 'Production' is not registered; create the stage before promotion

C. The MLflow server is down; restart the server

D. The model version 2 does not exist; create it first

Solution

1. Step 1: Analyze the error message

   The error says the stage 'Production' does not exist, meaning it is not registered in MLflow.

2. Step 2: Determine the fix

   You must create or register the 'Production' stage before promoting a model to it.

3. Final Answer:

   The stage 'Production' is not registered; create the stage before promotion -> Option B

4. Quick Check:

   Stage must exist before promotion [OK]

Hint: Check if target stage exists before promoting model [OK]

Common Mistakes:

• Assuming stage names are case-insensitive
• Blaming model version existence
• Ignoring server status

5. You want to automate promoting the best performing model version to Production only if it passes testing. Which approach best fits this requirement?

hard

A. Automatically promote every new model version to Production without testing

B. Manually run mlflow models transition-stage after testing

C. Use a CI/CD pipeline that runs tests, then promotes the model version to Production stage if tests pass

D. Delete all previous versions and keep only the latest model

Solution

1. Step 1: Understand automation and testing requirements

   Automation requires a pipeline that runs tests and promotes models only if tests pass.

2. Step 2: Evaluate options for automation

   Use a CI/CD pipeline that runs tests, then promotes the model version to Production stage if tests pass describes a CI/CD pipeline that tests and promotes automatically, matching the requirement.

3. Final Answer:

   Use a CI/CD pipeline that runs tests, then promotes the model version to Production stage if tests pass -> Option C

4. Quick Check:

   Automate with CI/CD pipeline and conditional promotion [OK]

Hint: Automate promotion with CI/CD pipeline after tests pass [OK]

Common Mistakes:

• Promoting without testing
• Manual promotion defeats automation
• Deleting versions unnecessarily