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A company is designing a canary deployment strategy for a payment processing API. The system must support automated gradual traffic shifting between…

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A company is designing a canary deployment strategy for a payment processing API. The system must support automated gradual traffic shifting between multiple Amazon Bedrock models based on real-time inference metrics, historical traffic patterns, and service health. The solution must be able to gradually increase traffic to new model versions. The system must increase traffic if metrics remain healthy and decrease traffic if the performance degrades below acceptable thresholds. The company needs to comprehensively monitor inference latency and error rates during the deployment phase. The company must also be able to halt deployments and revert to a previous model version without any manual intervention. Which solution will meet these requirements?
Answer options
A: Use Amazon Bedrock with provisioned throughput to host model versions. Configure an Amazon EventBridge rule to invoke an AWS Step Functions workflow when a new model version is released. Configure the workflow to shift traffic in stages, wait for a specified time period, and invoke an AWS Lambda function to check Amazon CloudWatch performance metrics. Configure the workflow to increase traffic if metrics meet thresholds and to trigger a traffic rollback if performance metrics fall below thresholds. Correct answer
B: Use AWS Lambda functions to invoke various Amazon Bedrock model versions. Use an Amazon API Gateway HTTP API with stage variables and weighted routing to shift traffic gradually. Use Amazon CloudWatch to monitor performance. Use external logic to adjust traffic and roll back if performance falls below thresholds.
C: Use Amazon SageMaker AI endpoint variants to represent multiple Amazon Bedrock model versions. Use variant weights to shift traffic. Use Amazon CloudWatch and SageMaker Model Monitor to trigger rollbacks. Use EventBridge to roll back deployments if an anomaly is detected.
D: Use Amazon OpenSearch Service to track inference logs. Configure OpenSearch Service to invoke an AWS Systems Manager Automation runbook to update Amazon Bedrock model endpoints to shift traffic based on inference logs.
Correct Answer:
A. Use Amazon Bedrock with provisioned throughput to host model versions. Configure an Amazon EventBridge rule to invoke an AWS Step Functions workflow when a new model version is released. Configure the workflow to shift traffic in stages, wait for a specified time period, and invoke an AWS Lambda function to check Amazon CloudWatch performance metrics. Configure the workflow to increase traffic if metrics meet thresholds and to trigger a traffic rollback if performance metrics fall below thresholds.
Explanation

This solution provides a complete, serverless, and automated canary deployment orchestration. Using Amazon Bedrock with provisioned throughput is suitable for a production API requiring consistent performance. AWS Step Functions is the ideal service for managing a stateful, multi-step workflow like a canary deployment. It can natively handle shifting traffic, waiting for a defined period, and executing conditional logic. An AWS Lambda function can query Amazon CloudWatch metrics (latency, errors). The Step Functions workflow can then automatically decide whether to increase traffic or initiate a rollback based on these metrics, meeting the requirement for a fully automated process without manual intervention.

Why Incorrect

B. This option relies on vague "external logic" for automation and rollback, making it less integrated and robust than a Step Functions-based orchestration.
C. Using Amazon SageMaker endpoints as a proxy for Bedrock models adds unnecessary complexity and is not the most direct or efficient architecture.
D. This proposes an unconventional architecture; AWS Systems Manager is not the standard tool for orchestrating application-level canary deployments.

References

1. AWS Step Functions Developer Guide, "Use Cases". The documentation explicitly lists "perform canary deployments" as a primary use case for Step Functions, highlighting its ability to orchestrate complex, stateful deployment workflows.

2. AWS Compute Blog, "Implementing canary deployments of serverless applications with AWS SAM and CodeDeploy". This article demonstrates the pattern of using a state machine (like Step Functions) to control gradual traffic shifting and automated rollbacks based on alarms.

3. Amazon Bedrock User Guide, "Provisioned Throughput". This section explains that Provisioned Throughput is designed for "large-scale and mission-critical applications" that require guaranteed latency and throughput, justifying its use in a payment processing API.

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