Promoting models between stages in MLOps - Mini Project: Build & Apply

Promoting Models Between Stages

📖 Scenario: You work in a team that builds machine learning models. Each model goes through stages: development, testing, and production. You want to write a simple script to promote a model from one stage to the next.

🎯 Goal: Build a script that stores models with their current stages, sets a target stage, updates the model's stage if allowed, and prints the updated stage.

📋 What You'll Learn

Create a dictionary called models with model names as keys and their current stages as values

Create a variable called target_stage with the value 'production'

Write a function called promote_model that takes a model name and promotes it to the target_stage only if the current stage is 'testing'

Print the updated stage of the model after promotion

💡 Why This Matters

🌍 Real World

Teams use model promotion scripts to safely move machine learning models through development, testing, and production stages.

💼 Career

Understanding model promotion is key for MLOps engineers to automate deployment pipelines and ensure model quality.

1

Create the initial models dictionary

Create a dictionary called models with these exact entries: 'modelA': 'development', 'modelB': 'testing', 'modelC': 'production'

MLOps

# Create the models dictionary with exact entries
# Your code here

Hint

Use curly braces {} to create a dictionary with keys and values.

2

Set the target stage variable

Create a variable called target_stage and set it to the string 'production'

MLOps

models = {'modelA': 'development', 'modelB': 'testing', 'modelC': 'production'}
# Set the target_stage variable to 'production'
# Your code here

Hint

Assign the string 'production' to the variable target_stage.

3

Write the promotion function

Write a function called promote_model that takes a parameter model_name. Inside the function, check if models[model_name] is 'testing'. If yes, update models[model_name] to target_stage. Use an if statement for this check.

MLOps

models = {'modelA': 'development', 'modelB': 'testing', 'modelC': 'production'}
target_stage = 'production'
# Define the promote_model function below
# Your code here

Hint

Use def to create the function. Use if to check the stage and update the dictionary value.

4

Promote a model and print the result

Call the function promote_model with the argument 'modelB'. Then print the stage of 'modelB' from the models dictionary using print(models['modelB']).

MLOps

models = {'modelA': 'development', 'modelB': 'testing', 'modelC': 'production'}
target_stage = 'production'

def promote_model(model_name):
    if models[model_name] == 'testing':
        models[model_name] = target_stage

# Call promote_model for 'modelB' and print its stage
# Your code here

Hint

Call the function with 'modelB' and then print the updated stage from the dictionary.

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