Why model versioning enables rollback in MLOps - Why It Works

Introduction

When you update a machine learning model, sometimes the new version may not work as expected. Model versioning helps you keep track of each model version so you can easily go back to a previous one if needed.

When a new model update causes worse predictions and you want to quickly restore the old model.

When you want to compare different model versions to see which performs better.

When you deploy models in production and need a safe way to switch between versions.

When multiple team members work on models and you want to avoid confusion about which version is current.

When you want to keep a history of all model changes for auditing or debugging.

Commands

This command serves the model version stored in the MLflow run with ID 1234567890abcdef on port 1234 so you can test or use it.

Terminal

mlflow models serve -m runs:/1234567890abcdef/model -p 1234

Expected Output

2024/06/01 12:00:00 INFO mlflow.pyfunc.model_server: Starting MLflow model server for model runs:/1234567890abcdef/model on port 1234

2024/06/01 12:00:00 INFO mlflow.pyfunc.model_server: Listening on port 1234...

→

-m - Specifies the model URI to serve

→

-p - Sets the port number for the server

This command rolls back to an older model version by serving the model from a previous run ID fedcba0987654321 on the same port.

Terminal

mlflow models serve -m runs:/fedcba0987654321/model -p 1234

Expected Output

2024/06/01 12:05:00 INFO mlflow.pyfunc.model_server: Starting MLflow model server for model runs:/fedcba0987654321/model on port 1234

2024/06/01 12:05:00 INFO mlflow.pyfunc.model_server: Listening on port 1234...

→

-m - Specifies the model URI to serve

→

-p - Sets the port number for the server

Key Concept

If you remember nothing else, remember: model versioning lets you safely switch back to any previous model to fix problems quickly.

Code Example

MLOps

import mlflow

# Log a new model version
with mlflow.start_run() as run:
    mlflow.sklearn.log_model(sk_model=model, artifact_path="model")
    print(f"Model version logged with run ID: {run.info.run_id}")

# Serve the latest model version
import os
os.system(f"mlflow models serve -m runs:/{run.info.run_id}/model -p 1234")

Common Mistakes

Not saving each model version separately before deploying.

Without separate versions, you cannot identify or restore an older model if the new one fails.

Always log and save each model version with a unique ID or tag before deployment.

Overwriting the same model path without versioning.

This loses the history of previous models and prevents rollback.

Use versioned model URIs or run IDs to keep each model distinct.

Forgetting to stop the current model server before starting a rollback version.

Two servers on the same port cause conflicts and errors.

Stop the running model server before serving a different model version on the same port.

Summary

Model versioning saves each model with a unique ID to keep track of changes.

You can serve any saved model version to test or deploy it.

Rollback means serving an older model version to fix issues quickly.

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