Blue-green deployment for models in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Blue-Green Deployment Basics

What is the main advantage of using a blue-green deployment strategy for machine learning models?

A It deploys models only during off-peak hours to reduce server load.

B It allows testing a new model version in production without downtime by switching traffic between two identical environments.

C It automatically retrains the model using live data without human intervention.

D It merges two different models into one to improve accuracy.

💡 Hint

Think about how to update a model without affecting users.

Blue-green deployment uses two identical environments (blue and green). One serves live traffic while the other is updated. Switching traffic avoids downtime and allows quick rollback.

💻 Command Output

intermediate

Interpreting Deployment Switch Command Output

You run a command to switch traffic from the blue environment to the green environment in your model deployment setup. What output indicates a successful switch?

MLOps

kubectl rollout status deployment/model-green
kubectl patch service model-service -p '{"spec":{"selector":{"env":"green"}}}'

A

deployment "model-green" successfully rolled out
service "model-service" patched

B

error: deployment "model-green" not found
service "model-service" patched

C

deployment "model-blue" successfully rolled out
service "model-service" patched

D

deployment "model-green" rolled out with warnings
service "model-service" patch failed

💡 Hint

Look for confirmation messages indicating success for both rollout and patch.

The command output shows the green deployment rolled out successfully and the service selector updated to route traffic to green.

❓ Configuration

advanced

Configuring Service Selector for Blue-Green Deployment

Which Kubernetes service selector configuration correctly routes traffic to the green environment in a blue-green deployment?

MLOps

apiVersion: v1
kind: Service
metadata:
  name: model-service
spec:
  selector:

A env: green

B version: blue

C env: blue

D version: green

💡 Hint

The selector must match the label of the green deployment pods.

The service selector uses the label 'env: green' to route traffic to pods labeled as green environment.

❓ Troubleshoot

advanced

Troubleshooting Traffic Not Switching in Blue-Green Deployment

After switching the service selector to the green environment, users still receive predictions from the blue model. What is the most likely cause?

A The model code in green pods is identical to blue pods.

B The green deployment pods crashed and are not running.

C The service selector was not updated correctly and still points to blue pods.

D The load balancer caches old responses and ignores the service selector.

💡 Hint

Check if the service selector matches the intended environment labels.

If the service selector still points to blue pods, traffic will not switch even if green pods are ready.

🔀 Workflow

expert

Blue-Green Deployment Workflow for Model Update

Arrange the steps in the correct order for performing a blue-green deployment of a new machine learning model version.

A 3,1,2,4

B 2,1,3,4

C 1,3,2,4

D 1,2,3,4

💡 Hint

Think about deploying, testing, switching traffic, then monitoring.

The correct workflow is to deploy the new model, test it, switch traffic, then monitor to ensure stability.

Practice

1. What is the main purpose of blue-green deployment in model updates?

easy

A. To run two models at the same time and combine their outputs

B. To switch traffic to a new model only after it is fully tested and ready

C. To update the model directly in the production environment without backup

D. To deploy models only during off-peak hours

Solution

1. Step 1: Understand blue-green deployment concept

   Blue-green deployment uses two separate environments to avoid downtime and risk during updates.

2. Step 2: Identify the key purpose

   The main goal is to switch traffic to the new model only after it is fully tested and ready, ensuring safety.

3. Final Answer:

   To switch traffic to a new model only after it is fully tested and ready -> Option B

4. Quick Check:

   Safe model update = A [OK]

Hint: Blue-green means switch only after testing new model [OK]

Common Mistakes:

• Thinking both models run and combine outputs
• Updating production without backup
• Deploying only during off-peak hours

2. Which command correctly switches traffic from the blue environment to the green environment in a Kubernetes service?

easy

A. kubectl set image deployment/model-deploy model=green-model:latest

B. kubectl delete deployment model-deploy-blue

C. kubectl rollout restart deployment/model-deploy-green

D. kubectl patch service model-service -p '{"spec":{"selector":{"env":"green"}}}'

Solution

1. Step 1: Understand traffic switching in Kubernetes

   Traffic is routed by the service selector labels pointing to the correct deployment environment.

2. Step 2: Identify the command that changes service selector to green

   The patch command updates the service selector to point to pods labeled with "env=green".

3. Final Answer:

   kubectl patch service model-service -p '{"spec":{"selector":{"env":"green"}}}' -> Option D

4. Quick Check:

   Change service selector = B [OK]

Hint: Patch service selector to green environment label [OK]

Common Mistakes:

• Restarting deployment does not switch traffic
• Setting image changes deployment but not traffic
• Deleting blue deployment before switch causes downtime

3. Given this simplified deployment script snippet, what will be the output after running it?

current_env = "blue"
new_env = "green"
if current_env == "blue":
    print(f"Switching traffic to {new_env} environment")
else:
    print(f"Switching traffic to {current_env} environment")

medium

A. Switching traffic to green environment

B. Switching traffic to undefined environment

C. Switching traffic to blue environment

D. No output

Solution

1. Step 1: Analyze the condition in the script

   The variable current_env is "blue", so the if condition is true.

2. Step 2: Determine the printed output

   Since current_env is "blue", it prints "Switching traffic to green environment" using new_env.

3. Final Answer:

   Switching traffic to green environment -> Option A

4. Quick Check:

   current_env == "blue" triggers green switch = C [OK]

Hint: Check if current_env is blue, then print green [OK]

Common Mistakes:

• Confusing current_env and new_env variables
• Assuming else branch runs when condition is true
• Ignoring f-string formatting

4. You tried to switch traffic to the green model environment but users still hit the blue model. What is the most likely error?

medium

A. The service selector was not updated to point to the green environment

B. The green model deployment was deleted accidentally

C. The blue environment pods crashed and restarted

D. The model version in green environment is outdated

Solution

1. Step 1: Understand traffic routing in blue-green deployment

   Traffic is controlled by the service selector labels pointing to the active environment.

2. Step 2: Identify why traffic still hits blue

   If users still hit blue, the service selector likely was not updated to green, so traffic stays on blue.

3. Final Answer:

   The service selector was not updated to point to the green environment -> Option A

4. Quick Check:

   Traffic routing depends on service selector = D [OK]

Hint: Check if service selector changed to green environment [OK]

Common Mistakes:

• Assuming green deployment deletion causes traffic to blue
• Confusing pod crashes with traffic routing
• Thinking model version affects routing directly

5. You want to implement blue-green deployment for a machine learning model with minimal downtime. Which sequence of steps is correct?

hard

A. Deploy new model to green, switch traffic immediately, then test and monitor

B. Delete blue environment, deploy new model to green, switch traffic, monitor performance

C. Deploy new model to green environment, test it, switch service selector to green, monitor, then delete blue

D. Deploy new model to blue environment, test it, switch service selector to blue, monitor, then delete green

Solution

1. Step 1: Deploy and test new model in green environment

   Deploying and testing in green ensures the new model works without affecting users.

2. Step 2: Switch traffic to green, monitor, then clean up blue

   Switching traffic only after testing reduces risk. Monitoring ensures stability before deleting blue.

3. Final Answer:

   Deploy new model to green environment, test it, switch service selector to green, monitor, then delete blue -> Option C

4. Quick Check:

   Test before switch, monitor after = A [OK]

Hint: Test green first, then switch traffic, then delete blue [OK]

Common Mistakes:

• Deleting blue before green is ready
• Switching traffic before testing
• Deploying new model to blue environment instead of green