Data-driven deployment decisions require comparing machine learning model performance against established baselines, balancing technical metrics with infrastructure costs, and executing progressive traffic transitions. In Vertex AI, teams evaluate model versions in the registry, deploy them to shared endpoints with controlled traffic splits, and monitor online inference behavior. Based on observed results, operators make concrete lifecycle choices: promote a validated model to full traffic, iterate on model artifacts to fix shortcomings, or roll back immediately to a stable version.
Model evaluation comparison is the process of quantitatively measuring and comparing predictive quality across different models, versions, or training runs. In Vertex AI Model Registry, users can compare up to 5 evaluation results simultaneously across models or versions of the same task type, such as classification, regression, or forecasting. Before routing production traffic, teams run offline evaluations to assess metrics like accuracy, error rates, or domain-specific thresholds against an existing baseline model. If a candidate model fails to match baseline quality during offline testing, development teams iterate on prompts, sampling parameters, or training configurations before progressing to live traffic.
Operational trade-off assessment involves balancing inference speed and throughput requirements against the cloud compute costs of serving infrastructure. When deploying a model as a DeployedModel on a Vertex AI endpoint, administrators configure hardware settings including machine type, accelerator type, and node scaling boundaries via minimum replica count (minReplicaCount) and maximum replica count (maxReplicaCount). Standby compute nodes defined by the minimum replica count incur charges continuously to ensure low-latency responses, even in the absence of traffic. Choosing larger machine specs or dedicated hardware accelerators increases inference performance and handles higher concurrency, but directly increases continuous serving expenses.
Deployment lifecycle decisions dictate how traffic shifts between candidate and existing models based on observed live behavior. By configuring the trafficSplit parameter on a single endpoint URL, operators deploy a new model alongside an existing model, routing a small percentage of requests (such as 10% or 20%) to execute a canary release or A/B test without altering client application endpoints. Teams choose among three primary deployment actions based on real-time and post-deployment metrics:
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StudyCertstrafficSplit percentage until it reaches 100%, completing a full rollout.trafficSplit so 100% of traffic routes back to the old DeployedModel ID, or invoke the undeploy operation (undeployModel) to remove the faulty model from the endpoint.A canary deployment is a controlled, incremental rollout strategy where a new model version is exposed to a small, increasing percentage of production traffic while its performance is monitored against defined health checks and success criteria. On Google Cloud Platform, this is implemented by splitting incoming inference request traffic between multiple deployed model versions using services like Cloud Run or Google Kubernetes Engine (GKE).
Traffic splitting is the core mechanism that enables canary deployments by distributing requests between model versions. In Cloud Run, you manage revisions, where each revision corresponds to a specific version of your model container. You directly assign a percentage of traffic to a new revision, and the Cloud Run service routes requests accordingly. In GKE, traffic splitting is typically managed by a Kubernetes ingress controller or service mesh, which directs traffic to different Deployments or Pods based on configured rules. The split can be based on the sender's IP address or, for more precise user stickiness, an HTTP cookie set by the application.
A progressive rollout requires pre-defining the parameters that control the deployment's progression and success. You must establish health checks (like latency percentiles or error rates) and success criteria (such as improved accuracy or business metrics) before beginning the rollout. You then configure rollback triggers, which are thresholds for key metrics that, if breached, automatically halt the rollout and revert all traffic to the stable version. Monitoring these metrics in real-time is essential; you use Cloud Monitoring to track custom metrics from your model and set up alerts based on the rollback triggers. The rollout proceeds in stages—for example, starting at 1% of traffic, then 5%, then 20%—with a waiting period at each stage to observe the new version's behavior under load.
To execute a gradual rollout on GCP, you use the traffic management features of specific services. For serverless model serving, Cloud Run allows you to deploy a new revision and then use the console or gcloud CLI to split traffic between it and the existing revision. You start with a small percentage (e.g., 5%) and manually or programmatically increase it over time as health checks pass. For Kubernetes-based serving, you use GKE with strategies like a canary Deployment. Here, you deploy the new model version alongside the old one and use an Ingress resource or the GKE Gateway controller to gradually shift traffic by adjusting the weight in the routing rules. The service's load balancer then directs requests based on these configured weights.
The primary purpose of a canary deployment is to mitigate risk by limiting the impact of a faulty new version. The gradual exposure allows you to detect issues that only appear under production load before they affect all users. A key tradeoff is that running multiple versions simultaneously consumes more resources and can complicate caching if static resources differ between versions. To avoid caching issues, you should use unique URLs for version-specific static assets or employ the Vary: Cookie HTTP header. The success of the rollout depends on continuous monitoring; if the new version's error rate spikes or its latency exceeds the old version's, the rollback triggers fire, and traffic is shifted back to the stable version automatically or by manual intervention.
A/B testing experiments for model comparison involve deploying multiple model versions to the same endpoint and splitting incoming traffic between them to evaluate performance differences in a controlled production environment. On Google Cloud Platform, this is achieved through Vertex AI endpoints by deploying multiple DeployedModel resources to a single endpoint and using traffic split percentages to route a portion of inference requests to each model variant.
To set up an A/B test, first deploy both the baseline model and the candidate model to the same endpoint. The endpoint serves as the single entry point that receives inference requests, while the traffic split determines what percentage of requests go to each deployed model. When deploying a model using the gcloud CLI, the --traffic-split flag controls how traffic is distributed; the temporary ID 0 represents the newly deployed model, while existing models use their assigned DEPLOYED_MODEL_ID. For example, --traffic-split=0=20,OLD_DEPLOYED_MODEL_ID=80 routes 20% of traffic to the new model and 80% to the existing model. All traffic percentages must sum to 100, and you can adjust these values over time as the experiment progresses.
The traffic split configuration happens at the endpoint level and determines the routing behavior for every prediction request. When you deploy a model to a new endpoint, the default traffic split is 100% to that model. When deploying to an existing endpoint with other deployed models, you must explicitly specify the traffic split for all models so that percentages add up to 100. The routing happens at the endpoint level, so client applications make requests to a single URL without needing to know which model version handles each request. This isolation means the same application code can test different model versions without modification, and you can gradually shift traffic from the control model to the treatment model as confidence in the new model grows.
When designing A/B tests, consider the compute resources allocated to each model variant. Each DeployedModel can have different machine types, accelerator configurations, and replica counts based on its requirements. The endpoint provides the service URL used for inference requests, for example: https://us-central1-aiplatform.googleapis.com/v1/projects/{project}/locations/{location}/endpoints/{endpoint}:predict. After deployment, the settings cannot be changed without redeploying the model, so you must specify machine type, minimum and maximum replica counts, and traffic split percentages at deployment time. Vertex AI Inference autoscaling scales the number of inference nodes based on concurrent requests, which means both deployed models can dynamically adjust to changing load while maintaining their assigned traffic percentages.
--traffic-split=0=20) controlling how inference requests are routed between model versions.Vary: Cookie header to avoid issues.You deploy both the baseline and candidate models to the same Vertex AI endpoint. Use the --traffic-split flag in the gcloud CLI to assign percentages, for example --traffic-split=0=20,OLD_DEPLOYED_MODEL_ID=80 routes 20% to the new model. Client applications continue to call the same endpoint URL, and the endpoint handles the routing.
Both use traffic splitting, but a canary deployment is a progressive rollout with automated rollback triggers based on health metrics (e.g., error rates, latency), while an A/B test is a controlled experiment that compares performance metrics (e.g., accuracy, business KPIs) and may not automatically roll back. Canary deployments often use Cloud Run revision traffic splitting or GKE ingress weights, while A/B tests typically use Vertex AI endpoints with traffic splits.
Choose iteration when the candidate model shows sub-optimal metrics (e.g., slightly lower accuracy) but does not cause errors or latency spikes. In that case, you can tune hyperparameters, improve prompts, or retrain the model offline, then redeploy for another test. Rollback is chosen when the new model causes immediate failures or severe degradation.
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