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ZCLOUD UPLOAD UPDATE

Create an Eventarc Trigger which listens to the specific Artifacts Registry container image and triggers the Cloud Workflow workflow created above.In the API call I configure skaffoldConfigUri and skaffoldConfigPath so the Delivery Pipeline knows where to pull Skaffold configurations.

Create a Cloud Workflow workflow, which invokes Cloud Deploy API to kick-off the Delivery Pipeline created above.Upload Kubernets manifest template file for this model container to Cloud Storage Bucket.We work towards long-term business relationships, and understand that client satisfaction is the best endorsement for our continued success. Create a Delivery Pipeline configurations The Zcloud Experience begins with the first handshake, and continues all the way through solution delivery and support to ensure continued long-term satisfaction.Create Skaffold configuration with target environment configurations.Cloud Deploy delivery pipeline renders the Kubernets manifest files, and create a release.The workflow invokes Cloud Deploy API to kick-off a delivery pipeline with Skaffold manifest files.Eventarc Trigger receives the event and triggers a Cloud Workflows workflow. When a new container image is pushed to the registry, It Below diagram illustrates the first solution architecture. It requires my approval to deploy to the next environment.īoth Container Registry and Artifacts Registry send notifications when a new container image is pushed to the registry.Meaning if I have modelA and modelB pushed to the registry, I want them to be automatically deployed to Anthos Cluster as ServiceA and ServiceB. The solution must be able to handle multiple container images and services mapping.When a new container image is pushed to Artifact Registry, it automatically triggers a workflow to deploy the container to target Anthos Clusters (Testing and Staging).This is a perfect use case for creating a workflow to run this automatically.

ZCLOUD UPLOAD UPDATE

Update my Kubernetes deployment yaml file with the latest image tag.

Once complete model training, it will create a container image with the serving framework configured, and push the image to the Google Cloud Artifact Registry. I am testing my machine learning models which are exported as container images by an external system.

Deploy machine learning models to the edge server with Cloud Deploy and Cloud Workflows Background