Promoting models between stages in MLOps - Commands & Configuration

Introduction

When you train a machine learning model, you want to test it carefully before using it for real. Promoting models between stages means moving a model from testing to production safely. This helps avoid mistakes and keeps your app working well.

When you want to move a model from development to testing without breaking anything

When you need to approve a model after checking its accuracy before using it live

When you want to keep track of which model version is currently in production

When you want to roll back to a previous model if the new one causes problems

When you want to automate the process of moving models through different quality checks

Commands

This command moves version 3 of 'my-model' to the Production stage, making it the live model used by your app.

Terminal

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

Expected Output

Model version '3' of model 'my-model' transitioned to stage 'Production'.

→

--model-name - Specifies the name of the model to promote

→

--version - Specifies the version number of the model to promote

→

--stage - Specifies the target stage to promote the model to

This command lists all versions of 'my-model' and shows their current stages, so you can verify the promotion.

Terminal

mlflow models list-versions --model-name my-model

Expected Output

Version  Stage

1        Staging

2        Archived

3        Production

→

--model-name - Specifies the model to list versions for

This command moves version 2 of 'my-model' to the Archived stage, marking it as no longer active.

Terminal

mlflow models transition-stage --model-name my-model --version 2 --stage Archived

Expected Output

Model version '2' of model 'my-model' transitioned to stage 'Archived'.

→

--model-name - Specifies the model name

→

--version - Specifies the version to archive

→

--stage - Specifies the Archived stage

Key Concept

If you remember nothing else from this pattern, remember: promoting a model means changing its stage label to control which version your app uses.

Code Example

MLOps

import mlflow
from mlflow.tracking import MlflowClient

client = MlflowClient()

# Promote model version 3 to Production
client.transition_model_version_stage(name='my-model', version=3, stage='Production')
print("Model version 3 promoted to Production")

Common Mistakes

Trying to promote a model version that does not exist

The command will fail because the version number is invalid or missing

Check available versions with 'mlflow models list-versions' before promoting

Promoting a model without testing it first

This can cause your app to use a bad model, leading to wrong predictions

Always test models in Staging before moving them to Production

Not archiving old model versions after promotion

Old versions may confuse the system or cause accidental use

Use the Archived stage to mark old versions as inactive

Summary

Use 'mlflow models transition-stage' to move a model version between stages like Staging, Production, or Archived.

Check model versions and their stages with 'mlflow models list-versions' to confirm promotions.

Always test models before promoting and archive old versions to keep your system clean.

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