Progressive Rollouts for AI-powered Applications

Question

Pulumi · Accepted Answer

Progressive rollouts for AI-powered applications involve deploying the application in phases or stages, which allow for monitoring and validation at each stage before proceeding to the next. This approach helps minimize risks, such as bugs or performance issues, by gradually introducing the application to a subset of the total user base or environment.

In the cloud, such rollouts can be handled via various services that support deployment strategies like canary releases, blue-green deployments, or feature flags. For example, in Google Cloud Platform, you can use Google Cloud Deploy to create a pipeline that facilitates progressive rollouts. Similarly, Azure and AWS offer services like Azure Deployment Slots or AWS CodeDeploy that enable similar capabilities.

Below is a Pulumi program that sets up a basic progressive rollout using Google Cloud Deploy service as an example. This service allows you to specify different target environments and deployments that can be gradually rolled out.

The program will do the following:

1. Set up a Google Cloud Deploy `DeliveryPipeline` that defines the overall deployment process.
2. Define a `Target` within the `DeliveryPipeline` where the deployment will take place.
3. Create a `Release` that references the artifacts to be deployed.
4. Cascade the `Release` through a `Rollout`, which will progressively deploy the application according to the defined deployment strategy.

Please note, the program assumes that you have configured your Pulumi credentials for Google Cloud and that necessary artifacts, like container images or code repositories, are in place.

```python
import pulumi
import pulumi_google_native as google_native

# Create a delivery pipeline for the AI application.
delivery_pipeline = google_native.clouddeploy.v1.DeliveryPipeline(
    "ai_app_delivery_pipeline",
    project="your-gcp-project-id",
    location="us-central1",
    description="Delivery pipeline for the AI-powered application",
    # Other optional properties can be set as needed.
)

# Define a target for the test environment within the delivery pipeline.
test_target = google_native.clouddeploy.v1.Target(
    "test_environment_target",
    project="your-gcp-project-id",
    location="us-central1",
    target_id="test-target",
    delivery_pipeline_id=delivery_pipeline.delivery_pipeline_id,
    require_approval=True,  # Require an approval before proceeding to the next phase.
    # In a real-world scenario, you might have specific target configuration such as GKE cluster details.
)

# Create a release to track and manage a set of deployable artifacts.
release = google_native.clouddeploy.v1.Release(
    "initial_ai_app_release",
    project="your-gcp-project-id",
    location="us-central1",
    release_id="initial-release",
    delivery_pipeline_id=delivery_pipeline.delivery_pipeline_id,
    # Skaffold configuration references the artifacts to be deployed.
    skaffold_config_path="skaffold.yaml",
    skaffold_config_version="latest",
    # The artifacts can include container images, source code repos, etc.
)

# Rollout the release progressively.
rollout = google_native.clouddeploy.v1.Rollout(
    "ai_app_rollout",
    project="your-gcp-project-id",
    location="us-central1",
    rollout_id="initial-rollout",
    target_id=test_target.target_id,
    release_id=release.release_id,
    delivery_pipeline_id=delivery_pipeline.delivery_pipeline_id,
    # Define the specific strategy for the rollout (e.g., percentage of traffic or user base).
    # For demonstration purposes, we assume a simple rollout strategy.
    # You would typically define a more complex strategy aligned with your specific use case.
    starting_phase_id=0,
)

# Export the URL of the deployed application as a stack output.
# Assume that the application exposes an endpoint that can be derived from the deployment.
pulumi.export("app_endoint_url", pulumi.Output.concat("https://", delivery_pipeline.location, "-", delivery_pipeline.name, ".cloudfunctions.net/ai_app"))
```

In this code, we define a delivery pipeline, target, release, and rollout for the AI application. This is a simple representation of what progressive deployments might look like, and in a real-world scenario, you'd want to define the rollout strategy more carefully, depending on your specific use case and needs.

Remember to replace `"your-gcp-project-id"` with your actual Google Cloud project ID, and customise the `skaffold.yaml` path and other properties as needed for your application. The `app_endpoint_url` export assumes the application is deployed to Cloud Functions, you'll need to adjust this part to reflect where your application is actually served from.

After the rollout, you should monitor your application's performance, user feedback, and other metrics to ensure the new version is functioning as intended before moving to the next deployment phase. You may also automate checks and rollbacks if the deployment does not meet certain criteria. This setup forms the basis for progressive rollouts enabling you to iterate on your AI application safely and efficiently.