ML Deployment Strategies: Shadow, Blue-Green, and Canary Patterns Quiz Quiz

Explore the essentials of machine learning deployment patterns such as shadow, blue-green, and canary releases. This easy-level quiz covers key concepts, use cases, and benefits to help solidify your understanding of safe and effective ML rollout strategies.

1. Identifying the Shadow Deployment Pattern

   In which deployment pattern does a new machine learning model receive live traffic alongside the current version without affecting end users, for example to monitor its real-world behavior before launch?

   1. Canary
   2. Blue-Green
   3. A/B Split
   4. Shadow

   Explanation: Shadow deployment sends real-time production traffic to the new model in parallel with the existing model, but only the original model's outputs reach users. This helps evaluate the new model safely. Blue-Green involves switching all users at once, Canary only routes a subset of users, and A/B Split is generally for experiments rather than deployment safety.

2. Purpose of Canary Deployment

   What is the main advantage of using the canary deployment pattern when launching a new ML model to production?

   1. Instant rollback capability
   2. No downtime during release
   3. Unlimited test users
   4. Gradual rollout with risk reduction

   Explanation: Canary deployment lets you introduce a new model to a small group of users first, minimizing risk and monitoring for issues before fully launching. While it may support rollback and can reduce downtime, those are not exclusive or primary features of canary strategies. The number of users is controlled, not unlimited.

3. Switching User Traffic in Blue-Green

   In a blue-green ML deployment, how is user traffic shifted between the old and new environments during the release process?

   1. Traffic switches instantly from blue to green
   2. Traffic gradually shifts over days
   3. Traffic is split equally between both
   4. Users choose which environment to access

   Explanation: Blue-green deployment involves shifting all user traffic from the current (blue) environment to the new (green) environment in one move, ensuring clean separation. The traffic is not split or shifted gradually—that process is more typical of canary deployment. Users do not control the environment, and the shift is not meant to span days.

4. Shadow vs. Canary Pattern

   Which key difference sets apart shadow deployment from canary deployment when updating an ML model?

   1. Shadow does not impact end users, canary does
   2. Canary never uses real traffic
   3. Shadow involves only synthetic data
   4. Canary always requires downtime

   Explanation: Shadow deployment duplicates live traffic for the new model without impacting user-facing results, while canary deployment exposes some end users to the new model deliberately. Canary does use real traffic and does not always cause downtime. Both can use live production data, so synthetic data is not an exclusive feature.

5. Rollbacks in Blue-Green Deployment

   Why is rolling back to a previous ML model version straightforward with blue-green deployment?

   1. Old environment remains untouched
   2. Shadow traffic is always used
   3. Models are merged during rollout
   4. Rollback requires retraining

   Explanation: The blue-green approach keeps the old environment available, so rolling back is as simple as routing traffic back to it. Blue-green does not merge models or require retraining for rollback. Shadow traffic is not a defining factor in blue-green deployments.

6. Minimizing User Impact with Canary

   During a canary deployment of a new ML model, how are users typically affected if an issue arises in the new version?

   1. Only a small user group is affected
   2. All users lose access
   3. Entire system shuts down
   4. Old model is automatically updated

   Explanation: With canary deployments, a limited portion of users interacts with the new model, so issues are contained and have minimal widespread impact. The system continues to operate for the majority. There is no automatic mass update of the old model, and the approach specifically avoids total shutdown.

7. Model Validation Using Shadow Pattern

   When an organization wants to compare the predictions of a new ML model with the current model on live data before switching, which pattern should it use?

   1. Greenfield
   2. A/B Split
   3. Canary
   4. Shadow

   Explanation: Shadow deployment allows side-by-side comparison of outputs on identical live inputs, enabling validation before affecting users. Canary exposes actual users to the new model, Greenfield generally refers to entirely new projects, and A/B Split is more for experiments than staged deployment.

8. Downtime in Blue-Green Deployment

   Which aspect of blue-green deployment helps reduce downtime during ML model updates?

   1. Switching entire traffic to new environment instantly
   2. Continuous gradual rollout
   3. Running both models simultaneously for users
   4. Using only test data

   Explanation: Blue-green deployment minimizes downtime by having the new environment ready and simply rerouting traffic in a single step. Running both simultaneously for users is not typical; typically only one environment receives production traffic. Test data use and gradual rollout are features of other patterns.

9. Selecting the Right Pattern for Risk Control

   Which deployment pattern is most appropriate for gradually releasing a new ML model to production while monitoring real user interactions?

   1. Shadow
   2. Blue-Green
   3. Red-Black
   4. Canary

   Explanation: Canary deployment is specifically designed for gradual release to a small segment while monitoring the behavior of the new model in real usage. Shadow is for behind-the-scenes testing, blue-green is for instant switchovers, and 'Red-Black' is not a commonly used ML deployment pattern.

10. Primary Goal of Shadow Deployment

    What is the main objective of employing shadow deployment for a new ML model in a real-world application?

    1. To minimize hardware costs
    2. To assess model performance without affecting users
    3. To optimize training data size
    4. To provide user-controlled rollout

    Explanation: Shadow deployment's main purpose is to test and monitor the new model's performance using real traffic, while keeping the user experience unchanged. It does not focus on minimizing hardware costs, giving users control, or directly optimizing training data size.