Amazon SageMaker endpoints support multiple production variants, which is the feature designed for A/B testing. By updating the existing endpoint configuration to add the new model as a second ProductionVariant, the firm can use the InitialVariantWeight parameter to divert a small percentage of live inference traffic to the new model. This allows for evaluating its accuracy and latency on real-world data. This approach is seamless to the client application, as it continues to invoke the same single endpoint URL. The existing model handles the majority of the traffic, ensuring its throughput is not significantly impacted, thus meeting all the scenario's requirements.

A. Deploying separate endpoints would require client-side changes to call the new endpoint or a complex traffic duplication mechanism, violating the "no changes to how clients invoke" requirement.

B. This describes a blue/green deployment strategy, which replaces the old model entirely rather than running both simultaneously for comparative evaluation on live traffic.

C. While technically feasible, this adds an unnecessary component (API Gateway) and complexity. SageMaker provides a native, more direct solution for A/B testing that doesn't require an intermediary service.

1. AWS SageMaker Developer Guide, "Test models in production": This section explicitly details the use of production variants for A/B testing. It states, "To test models in production, you can deploy multiple models to the same SageMaker endpoint... You configure the production variants to distribute traffic between two or more models by providing the percentage of traffic to route to each variant."

Source: AWS SageMaker Developer Guide, Section: "Deploy models for inference" -> "Test models in production".

2. AWS SageMaker API Reference, CreateEndpointConfig Action: The documentation for this API call defines the ProductionVariants parameter. It specifies that you can provide a list of variants, each with its own ModelName, InstanceType, and InitialVariantWeight, which "determines the proportion of traffic that flows to a variant."

Source: AWS SageMaker API Reference, CreateEndpointConfig -> ProductionVariants -> InitialVariantWeight.

3. AWS Machine Learning Blog, "A/B testing machine learning models in production using Amazon SageMaker": This official blog post provides a practical walkthrough. It confirms, "Amazon SageMaker makes this easy by allowing you to specify multiple models, or model variants, in the same endpoint. You can distribute traffic to each model by specifying the traffic distribution, or weight, for each model."

Source: AWS Machine Learning Blog, Post Title: "A/B testing machine learning models in production using Amazon SageMaker", Published: 20 OCT 2020.