Why model versioning enables rollback in MLOps - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to save a model version using MLflow.

MLOps

mlflow.sklearn.log_model(model, '[1]')

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

Aversion

Bmodel_name

Cartifact_path

Dmodel_version

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'model_version' instead of the artifact path.

Confusing model name with artifact path.

The artifact_path is where the model is saved in MLflow. This helps track versions.

2fill in blank

medium

Complete the code to load a specific model version for rollback.

MLOps

model = mlflow.pyfunc.load_model('models:/[1]/1')

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

Amodel_name

Bmodel_version

Cversion

Dartifact_path

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'version' instead of model name.

Using artifact path in the registry URI.

The model_name is used in the model registry URI to specify which model to load.

3fill in blank

hard

Fix the error in the code to rollback to a previous model version.

MLOps

mlflow.register_model('models:/my_model/[1]', 'my_model')

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

Alatest

Bcurrent

Cproduction

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'latest' which points to newest, not rollback.

Using stage names instead of version numbers.

To rollback, specify the exact version number like '1' to register that version.

4fill in blank

hard

Fill both blanks to update the model stage to enable rollback.

MLOps

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

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

A1

B2

CProduction

DStaging

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong version number.

Using lowercase stage names.

Version '1' is moved to 'Production' stage to rollback to that version.

5fill in blank

hard

Fill all three blanks to create a dictionary mapping model versions to stages for rollback.

MLOps

version_stages = [1]: '[2]', [3]: 'Archived'}

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

A1

BProduction

C2

DStaging

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up version numbers and stage names.

Using wrong stage names.

This dictionary maps version 1 to Production and version 2 to Archived, enabling rollback management.

Practice

1. Why is model versioning important in machine learning projects?

easy

A. It automatically improves the model's accuracy.

B. It converts models into different programming languages.

C. It deletes old models to save space.

D. It allows you to save and track different versions of a model.

Solution

1. Step 1: Understand model versioning purpose

   Model versioning means saving different copies of a model with unique names or tags.

2. Step 2: Identify the benefit of versioning

   This helps track changes and allows going back to a previous model if needed.

3. Final Answer:

   It allows you to save and track different versions of a model. -> Option D

4. Quick Check:

   Model versioning = Save and track versions [OK]

Hint: Model versioning means saving copies to track changes [OK]

Common Mistakes:

• Thinking versioning improves accuracy automatically
• Believing versioning deletes old models
• Confusing versioning with code translation

2. Which of the following is the correct way to name a model version for rollback purposes?

easy

A. model_v1.0

B. model-final

C. model_latest

D. modelbackup

Solution

1. Step 1: Identify clear version naming

   Using a version number like 'v1.0' clearly marks the model version.

2. Step 2: Compare naming clarity

   Names like 'model-final' or 'model_latest' are vague and do not specify version order clearly.

3. Final Answer:

   model_v1.0 -> Option A

4. Quick Check:

   Clear version numbers = model_v1.0 [OK]

Hint: Use clear version numbers like v1.0 for rollback [OK]

Common Mistakes:

• Using vague names without version numbers
• Assuming 'latest' is a fixed version
• Ignoring semantic versioning

3. Given the following model versions saved: model_v1.0, model_v1.1, and model_v2.0. If model_v2.0 causes errors, what will happen if you rollback to model_v1.1?

medium

A. The system will use the stable model_v1.1 without errors.

B. The system will still use model_v2.0 causing errors.

C. The rollback will delete all previous models.

D. Rollback will upgrade model_v2.0 automatically.

Solution

1. Step 1: Understand rollback purpose

   Rollback means switching back to a previous stable model version.

2. Step 2: Apply rollback to model_v1.1

   Switching to model_v1.1 avoids errors caused by model_v2.0.

3. Final Answer:

   The system will use the stable model_v1.1 without errors. -> Option A

4. Quick Check:

   Rollback to stable version = no errors [OK]

Hint: Rollback uses previous stable model to avoid errors [OK]

Common Mistakes:

• Thinking rollback deletes models
• Believing rollback upgrades models
• Assuming rollback keeps faulty version active

4. You tried to rollback to a previous model version but the system still uses the new faulty model. What is the most likely cause?

medium

A. Model versioning does not support rollback.

B. The previous model version was deleted.

C. The rollback command was not executed properly.

D. The new model version is always used by default.

Solution

1. Step 1: Check rollback execution

   If rollback was not run correctly, the system stays on the faulty model.

2. Step 2: Verify model versions

   If the previous version exists, the issue is likely the rollback command.

3. Final Answer:

   The rollback command was not executed properly. -> Option C

4. Quick Check:

   Failed rollback = command error [OK]

Hint: Ensure rollback command runs successfully to switch versions [OK]

Common Mistakes:

• Assuming rollback deletes models
• Believing new model is always forced
• Thinking rollback is unsupported

5. You have three model versions: v1.0, v1.1, and v2.0. After deploying v2.0, performance dropped. You want to rollback but keep track of this failed attempt. What is the best practice?

hard

A. Overwrite v1.1 with v2.0 to keep latest only.

B. Tag v2.0 as 'failed' and deploy v1.1 again.

C. Delete v2.0 and redeploy v1.1 without tags.

D. Deploy v1.0 without tagging any versions.

Solution

1. Step 1: Preserve failed model version

   Tagging v2.0 as 'failed' keeps record of the issue.

2. Step 2: Rollback safely

   Deploying v1.1 again restores stable performance while tracking history.

3. Final Answer:

   Tag v2.0 as 'failed' and deploy v1.1 again. -> Option B

4. Quick Check:

   Tag failed + rollback stable = best practice [OK]

Hint: Tag failed versions, rollback to stable, keep history [OK]

Common Mistakes:

• Deleting failed versions losing history
• Overwriting stable versions
• Ignoring version tags