To implement blue/green deployment for embedding models, you need to maintain two identical environments (blue and green) and switch traffic between them after validating the new model version. Start by setting up separate infrastructure for both environments, such as deploying the current model (blue) and the updated model (green) on separate servers, containers, or cloud instances. Use a load balancer, API gateway, or feature store to route incoming requests to the active environment. For example, if your embedding model is served via an API, configure the router to direct traffic to the blue environment by default. Deploy the updated model to the green environment, run tests, and only switch traffic once it’s verified. This approach minimizes downtime and allows instant rollback if issues arise.
A practical implementation might involve Kubernetes or cloud services like AWS SageMaker. Suppose you’re using Kubernetes: Deploy the current model as a “blue” service with a stable endpoint (e.g., embeddings-v1). Then, deploy the updated model as a “green” service with a temporary endpoint (e.g., embeddings-v2-test). Test the green deployment using a subset of traffic or automated integration tests to validate output consistency, latency, and accuracy. If the tests pass, update the router’s configuration to point the main endpoint to the green service. For embedding models, ensure the new version’s output dimensions and semantics align with downstream applications. For instance, if a search system relies on cosine similarity between embeddings, verify that the green model produces vectors with the same length and distribution as the blue version. Tools like feature stores (e.g., Feast) can help manage versioned embeddings and simplify A/B testing.
Key considerations include data compatibility, monitoring, and rollback strategies. If the green model requires retraining with new data, ensure the training pipeline is versioned and reproducible. During the transition, log performance metrics (e.g., latency, error rates) and business metrics (e.g., search relevance scores) for both environments. Use monitoring tools like Prometheus or cloud-specific services to detect anomalies. If the green model underperforms, revert traffic to the blue environment immediately. For embedding models, also consider caching: If clients cache embeddings, include a version identifier in cache keys to prevent mixing results from different models. Finally, automate the deployment process using CI/CD pipelines (e.g., GitHub Actions or GitLab CI) to reduce human error. For example, a pipeline could deploy the green environment, run validation tests, and update the router configuration automatically upon success.