Evaluation metadata association, governance, and access control are practices for attaching validation results to registered models and controlling who can access or change them. This ensures only models that meet quality and safety standards are deployed and that all actions are logged and secure.
Attaching evaluation metadata means linking validation outputs directly to a specific model version in the Gemini Enterprise Agent Platform Model Registry. This metadata includes performance metrics, safety benchmark scores, and model card documentation. These results come from testing the model on separate data and checking for fairness and operational readiness. By storing this information with the model, teams can verify a model passes all required thresholds before approving it for deployment, creating a trustworthy and repeatable workflow.
Model governance is enforced by setting up Identity and Access Management (IAM) roles, applying resource labels, and turning on audit logging. IAM roles define exactly which users or service accounts can register, view, update, or deploy specific model versions. Resource labels, like tags for project or team, help organize models and apply policies consistently. Cloud Audit Logs record every administrative action and data access, creating a complete trail for compliance checks and troubleshooting. These controls apply security policies across all model registry setups, ensuring the model lifecycle follows the organization's rules.
Access control and governance policies manage how a model moves through its lifecycle states, such as from development to staging to production. IAM policies decide which roles can trigger these state changes, like promoting a model. The decision to deploy a model depends on checking its attached metadata against set thresholds. If the metrics pass, authorized users can advance the model. This gated process, combined with full logging, ensures every change is authorized, backed by data, and recorded, keeping the model inventory secure and reliable.
Model artifact registration and version lineage tracking involve uploading trained machine learning models to the central Gemini Enterprise Agent Platform Model Registry and keeping detailed records of their origin and changes. This process ensures models can be deployed reproducibly and their history can be fully audited.
The Gemini Enterprise Agent Platform Model Registry is a central repository for managing machine learning models. To use a model for predictions (inference), you must first upload your trained model to this registry. If you train your model using the platform's own custom training service, the model can be automatically imported when the job finishes. If the model was trained elsewhere, you can upload it manually by pointing the registry to the model files stored in Cloud Storage.
You can register models through several methods depending on your workflow. When using the platform's training job classes, running the job automatically creates, trains, and registers the model. For models trained outside the platform, you use specific upload methods from the SDK, like Model.upload_scikit_learn_model_file. You can also retrieve an existing model from the registry using its name or ID.
When you import a model to the registry, you must associate it with a container so the platform can serve prediction requests. Prebuilt containers are ready-to-use servers for common ML frameworks and handle only the prediction step. For more complex needs, you can use a custom container that you build yourself, which must run a specific web server. A simpler middle option is a custom inference routine, where you provide your data transformation code and the SDK builds a custom container for you.
The Model Registry allows you to create multiple versions of the same model, which is key for tracking history and enabling rollbacks. You create a new version by specifying a parent model or by setting version aliases and descriptions during training. In the registry, you can view all versions, set a default version, and add easy-to-remember aliases to versions. Training pipelines package the training job and automatically register the resulting model artifact.
To ensure full auditability and reproducibility, you must track the lineage of everything involved in creating a model. This includes the source code, pipeline runs, datasets used, and all output artifacts like model checkpoints and evaluation results. You should record hyperparameters, configurations, dataset statistics, and model evaluation metrics. For governance, use a clear versioning scheme and attach all this metadata to each model version. Store the actual model files and container images in Artifact Registry to leverage its versioning features.
After a model is registered, you must deploy it to an endpoint before it can serve predictions. You use the Model.deploy method to deploy the model. Once the endpoint is active, it can accept prediction requests. For processing large batches of data, you can submit a batch inference job. Some tuned models are automatically deployed to a shared public endpoint managed by the platform.
Model version aliasing and deployment routing let teams manage model updates and releases safely without breaking applications that depend on them. Within the Gemini Enterprise Agent Platform Model Registry, version aliases are changeable labels that point to specific, unchangeable model versions. This setup allows engineers to move models through different environments and control how traffic is distributed across multiple model versions on a single serving endpoint.
Model version aliases are named pointers, like staging or prod, that refer to specific version numbers in the registry. Instead of applications hardcoding a version number, they reference the alias. When a new, validated model version is ready, engineers simply update the alias to point to the new number. This means the endpoint URI and client configurations don't need to change, preventing errors and ensuring requests automatically go to the correct model.
Lifecycle promotion is the structured process of moving a model through stages like development, staging, and production. As a model passes tests and benchmarks, automated pipelines update its aliases to show its readiness. A new model first gets a development alias. After passing integration tests, the staging alias is reassigned to it. Finally, after all evaluations are successful, the production alias is updated to point to it, making it live without needing to redeploy the model files.
Deployment routing lets a single serving endpoint host several model versions at once and split incoming traffic between them. By configuring a traffic split, operators assign percentage weights to each version. In a canary rollout, a new candidate version is deployed alongside the stable one, and a small portion of live traffic (e.g., 5%) is routed to it. Monitoring systems watch the candidate's performance. If it performs well, traffic can be gradually increased until it handles 100%, allowing safe verification under real conditions.
Rollback strategies provide a fast way to recover if a new model version has problems. If a performance issue is detected, operators can immediately update the endpoint's traffic split to send 100% of traffic back to the previous stable version. Alternatively, they can reassign the production alias in the registry to point back to the old version number. Both methods work instantly, avoiding downtime because the serving endpoint itself stays running while the traffic routing changes.
StudyCerts
StudyCertsstaging or prod) are mutable labels that point to immutable version numbers, allowing lifecycle promotion without changing client configurations.Attaching evaluation metadata provides an auditable record of a model's validation results, such as its accuracy and safety scores. This allows teams to verify the model meets all predefined quality and operational thresholds before approving it for deployment, ensuring only reliable models are promoted.
Model version aliases act as human-readable pointers (e.g., prod) to specific version numbers. Downstream applications reference the alias, not a fixed number. When a new model is ready, engineers update the alias target, automatically routing all future requests to the new version without needing to update application code or endpoint URIs.
A canary rollout is a safe deployment strategy where a new model version is released alongside the current version, but only a small percentage of live traffic (like 5%) is routed to it. This is configured by setting a traffic split on the serving endpoint. The new version's performance is monitored under real load, and if it's stable, traffic is gradually increased until it fully replaces the old version.
The two main rollback methods are: 1) Updating the traffic split on the serving endpoint to immediately route 100% of traffic back to the previous stable model version. 2) Reassigning the production alias in the Model Registry to point back to the version number of the previous stable model. Both methods redirect traffic away from the faulty version without taking the endpoint offline.
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