5.1.5. Deployment Options (Direct, Blue/Green, Canary)

First Principle: Strategic deployment options fundamentally minimize risk and ensure continuous availability during model updates by controlling traffic flow and enabling safe rollbacks.

Deploying new versions of ML models to production requires careful strategies to minimize downtime, reduce risk, and ensure a smooth transition. Amazon SageMaker supports various deployment options.

Key Deployment Options:

• Direct Update (In-Place Update):
  • Method: The new model version directly replaces the old one on the existing endpoint instances.
  • Pros: Simplest to implement.
  • Cons: Can lead to downtime or performance degradation during the update. High risk if the new model has issues. Not recommended for critical production workloads.
  • AWS: SageMaker allows this by updating the endpoint configuration.
• Blue/Green Deployment:
  • Method: A new, identical "Green" environment (new endpoint instances with the new model) is set up alongside the existing "Blue" environment (old model). Once the Green environment is validated, traffic is fully switched from Blue to Green. The Blue environment is kept for a rollback option.
  • Pros: Zero downtime. Easy and fast rollback to the old version if issues arise. New environment can be fully tested before traffic switch.
  • Cons: Requires double the infrastructure resources temporarily.
  • AWS: Achieved in SageMaker by creating a new endpoint configuration and then updating the existing endpoint to use the new configuration. SageMaker handles the traffic shift.
• Canary Deployment:
  • Method: A small percentage of live traffic is gradually shifted to the new model version (the "canary"). If the canary performs well (monitored by metrics), more traffic is shifted until 100% is on the new version. If issues are detected, traffic is immediately reverted to the old version.
  • Pros: Minimizes risk by exposing the new model to only a small subset of users initially. Allows for real-world testing and quick rollback.
  • Cons: More complex to set up and monitor. Requires robust monitoring and alerting.
  • AWS: SageMaker supports this by allowing you to specify traffic weights for different production variants within an endpoint configuration. You can gradually increase the weight for the new model.
• A/B Testing:
  • Method: Similar to canary, but the goal is to compare the performance of two or more model versions (or even different algorithms) over a longer period to determine which performs best for a specific business metric. Traffic is split between variants.
  • AWS: SageMaker endpoints support multiple production variants with configurable traffic distribution, making A/B testing straightforward.

Choosing the Right Strategy:

• Direct Update: Only for non-critical applications or development environments.
• Blue/Green: For critical applications requiring zero downtime and quick rollback, but can tolerate temporary double cost.
• Canary: For critical applications where you want to minimize risk and test in production with real traffic, but have robust monitoring.
• A/B Testing: When you need to empirically compare different models or strategies over time to optimize a business metric.

Scenario: You are deploying a new version of a critical fraud detection model. You need to ensure zero downtime during the update and have an immediate rollback option if the new model introduces unforeseen issues. You also want to test a new recommendation model with a small percentage of live user traffic before a full rollout.

Reflection Question: How do strategic deployment options like Blue/Green (for zero-downtime rollback) and Canary (for phased rollout with risk mitigation) fundamentally minimize risk and ensure continuous availability during model updates by controlling traffic flow and enabling safe rollbacks?

💡 Tip: For the exam, understand the trade-offs: Direct is simple but risky; Blue/Green is safe but costly; Canary is safe and cost-effective for phased rollouts but more complex to monitor.