MLflow Model Registry in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to register a model in MLflow Model Registry.

MLOps

mlflow.register_model(model_uri, [1])

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

Amodel_version

Brun_id

Cmodel_name

Dartifact_path

Attempts:

3 left

💡 Hint

Common Mistakes

Using model_version instead of model_name

Confusing run_id with model_name

The register_model function requires the model_name to register the model in the registry.

2fill in blank

medium

Complete the code to transition a model version to 'Production' stage.

MLOps

client.transition_model_version_stage(name, version, [1])

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

ANone

BArchived

CStaging

DProduction

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Archived' instead of 'Production'

Using 'None' which is invalid

The transition_model_version_stage method requires the target stage, here 'Production', to move the model version to production.

3fill in blank

hard

Fix the error in the code to fetch the latest model version in 'Staging' stage.

MLOps

latest_version = client.get_latest_versions(name, [1])[0]

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

Astages=['Archived']

Bstages=['Staging']

Cstages=['Production']

Dstages=['None']

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Production' instead of 'Staging'

Using 'None' which is invalid

The get_latest_versions method requires the stages parameter to filter versions. To get the latest in 'Staging', use stages=['Staging'].

4fill in blank

hard

Fill both blanks to create a dictionary of model versions and their stages for a registered model.

MLOps

versions = {v.version: v.[1] for v in client.get_latest_versions([2])}

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

Acurrent_stage

Bmodel_name

Cname

Dstage

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'stage' instead of 'current_stage'

Using 'model_name' instead of 'name' in the method call

Each version object has a current_stage attribute. The get_latest_versions method requires the model's name parameter.

5fill in blank

hard

Fill all three blanks to update the description of a specific model version.

MLOps

client.update_model_version(name=[1], version=[2], description=[3])

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

A"MyModel"

B3

C"Updated description for version 3"

D"NewModel"

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong model name string

Passing version as a string instead of an integer

Not quoting the description string

The update_model_version method requires the model name as a string, the version number, and the new description string.

Practice

1. What is the primary purpose of the MLflow Model Registry?

easy

A. To train machine learning models automatically

B. To visualize data for machine learning

C. To organize, track, and manage machine learning models and their versions

D. To store raw datasets for training

Solution

1. Step 1: Understand the role of MLflow Model Registry

   The Model Registry is designed to keep track of models, their versions, and lifecycle stages.

2. Step 2: Compare with other options

   Training models, visualizing data, and storing raw data are not functions of the Model Registry.

3. Final Answer:

   To organize, track, and manage machine learning models and their versions -> Option C

4. Quick Check:

   Model Registry purpose = Organize and track models [OK]

Hint: Model Registry = model tracking and version control [OK]

Common Mistakes:

• Confusing Model Registry with training or data storage
• Thinking it handles data visualization
• Assuming it automatically trains models

2. Which of the following is the correct command to register a model named my_model with MLflow Model Registry using the Python API?

easy

A. mlflow.create_model(name='my_model')

B. mlflow.register_model(model_uri='runs:/1234/my_model', name='my_model')

C. mlflow.add_model_version('my_model')

D. mlflow.model.register('my_model')

Solution

1. Step 1: Recall the MLflow Python API for registering models

   The correct function is mlflow.register_model() with parameters model_uri and name.

2. Step 2: Check the options for syntax correctness

   Only mlflow.register_model(model_uri='runs:/1234/my_model', name='my_model') uses the correct function and parameters. Others are invalid or do not exist.

3. Final Answer:

   mlflow.register_model(model_uri='runs:/1234/my_model', name='my_model') -> Option B

4. Quick Check:

   Register model = mlflow.register_model() [OK]

Hint: Use mlflow.register_model() with model_uri and name [OK]

Common Mistakes:

• Using non-existent functions like create_model or add_model_version
• Incorrect function names or missing parameters
• Confusing model registration with model creation

3. Given the following Python code snippet using MLflow Model Registry, what will be the output?

from mlflow import MlflowClient
client = MlflowClient()
model_versions = client.get_latest_versions(name='my_model', stages=['Production'])
print(len(model_versions))

medium

A. Zero, because the method returns an empty list always

B. The total number of all model versions regardless of stage

C. An error because get_latest_versions requires no parameters

D. The number of model versions currently in the Production stage

Solution

1. Step 1: Understand the method get_latest_versions

   This method returns the latest versions of a model filtered by the specified stages.

2. Step 2: Analyze the code behavior

   The code filters for versions in the 'Production' stage and prints how many such versions exist.

3. Final Answer:

   The number of model versions currently in the Production stage -> Option D

4. Quick Check:

   get_latest_versions with stages filters versions [OK]

Hint: get_latest_versions(name, stages) filters by stage [OK]

Common Mistakes:

• Assuming it returns all versions without filtering
• Thinking the method takes no parameters
• Believing it always returns zero

4. You try to transition a model version to the 'Staging' stage using this code:

client.transition_model_version_stage(name='my_model', version='2', stage='Staging')

But you get a TypeError. What is the likely cause?

medium

A. The version parameter should be an integer, not a string

B. The stage name 'Staging' is invalid

C. The method name is incorrect; it should be change_stage

D. The client object is not initialized

Solution

1. Step 1: Check the method signature for transition_model_version_stage

   The version parameter must be an integer representing the model version number.

2. Step 2: Identify the error cause

   Passing version='2' as a string causes a TypeError; it should be version=2 as an integer.

3. Final Answer:

   The version parameter should be an integer, not a string -> Option A

4. Quick Check:

   Version parameter type = int [OK]

Hint: Pass version as int, not string, to avoid TypeError [OK]

Common Mistakes:

• Passing version as string instead of integer
• Using wrong method name
• Assuming stage names are invalid
• Not initializing the client object

5. You want to automate deployment by moving the latest model version from 'Staging' to 'Production' only if its accuracy metric is above 0.9. Which MLflow Model Registry workflow correctly implements this logic?

hard

A. Retrieve latest 'Staging' version, check accuracy metric, then transition to 'Production' if > 0.9

B. Transition all versions to 'Production' without checking metrics

C. Delete all 'Staging' versions and register a new model in 'Production'

D. Manually download the model and deploy without using Model Registry stages

Solution

1. Step 1: Identify the correct workflow for conditional deployment

   You must get the latest model version in 'Staging' and check its accuracy metric before promoting.

2. Step 2: Understand why other options are incorrect

   Transitioning all versions blindly ignores quality checks; deleting and manual deployment bypasses registry benefits.

3. Final Answer:

   Retrieve latest 'Staging' version, check accuracy metric, then transition to 'Production' if > 0.9 -> Option A

4. Quick Check:

   Conditional stage transition based on metric = correct workflow [OK]

Hint: Check metric before stage transition to automate deployment [OK]

Common Mistakes:

• Skipping metric check before promotion
• Promoting all versions blindly
• Deleting versions unnecessarily
• Ignoring Model Registry stages