Why model versioning enables rollback in MLOps - Visual Breakdown

Process Flow - Why model versioning enables rollback

Train New Model

↓

Assign Version Number

↓

Save Model with Version

↓

Deploy Model

↓

Detect Issue?

No→Continue Using Current Model

Yes↓

Rollback to Previous Version

↓

Deploy Previous Model Version

This flow shows how each model gets a version number when saved. If a problem happens, you can switch back to an older version easily.

Execution Sample

MLOps

train_model()
version = 3
save_model(model, version)
deploy_model(version)
if issue_detected:
  deploy_model(version - 1)

This code trains a model, saves it with a version, deploys it, and rolls back to the previous version if an issue is detected.

Process Table

Step	Action	Version Assigned	Deployment Version	Issue Detected	Rollback Action
1	Train model	-	-	No	-
2	Assign version	3	-	No	-
3	Save model with version	3	-	No	-
4	Deploy model version	3	3	No	-
5	Detect issue	3	3	Yes	-
6	Rollback to previous version	3	2	Yes	Deployed version 2

💡 Rollback completed by deploying previous model version 2 after issue detected in version 3

Status Tracker

Variable	Start	After Step 2	After Step 4	After Step 6
version	-	3	3	3
deployment_version	-	-	3	2
issue_detected	False	False	False	True

Key Moments - 3 Insights

Why do we assign a version number to each model?

Assigning a version number (see execution_table step 2) helps us identify and manage different models easily, enabling rollback if needed.

What happens when an issue is detected after deployment?

When an issue is detected (step 5), the system rolls back to the previous stable version (step 6) to keep the service working well.

Does rollback change the saved model version number?

No, rollback changes the deployed version but the saved model version remains the same (see variable_tracker deployment_version changes, version stays at 3).

Visual Quiz - 3 Questions

Look at the execution table, what is the deployment version right after the initial deployment?

A No version deployed

B Version 2

C Version 3

D Version 1

💡 Hint

Check the 'Deployment Version' column at step 4 in the execution_table.

At which step does the rollback happen according to the execution table?

A Step 5

B Step 6

C Step 4

D Step 3

💡 Hint

Look for the step where 'Rollback Action' is noted in the execution_table.

If no issue was detected, what would happen to the deployment version?

A It would stay at the new version

B It would rollback to previous version

C It would be deleted

D It would increment automatically

💡 Hint

Refer to the 'Issue Detected' and 'Rollback Action' columns in the execution_table.

Concept Snapshot

Model versioning means saving each model with a unique number.
This lets you track and manage models easily.
If a new model causes problems, you can rollback to an older version.
Rollback means deploying a previous stable model.
This keeps your system reliable and safe.

Full Transcript

Model versioning is a way to save each trained model with a unique version number. When you deploy a model, you use its version number to identify it. If the deployed model causes problems, you can detect the issue and rollback by deploying an earlier version. This rollback process helps keep your system stable and reliable. The execution table shows each step: training, assigning version, saving, deploying, detecting issues, and rolling back. Variables like version and deployment_version track the current state. Beginners often wonder why version numbers are needed or how rollback works. The key is that versioning allows easy switching between models without confusion.

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