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.