Progressive Delivery for Machine Learning APIs With GCP Cloud Deploy

Solution Overview

In this solution, we will set up a progressive delivery pipeline for Machine Learning APIs using Google Cloud Deploy. The key services involved include Google Cloud Deploy for managing the delivery pipeline, Google Kubernetes Engine (GKE) for hosting the APIs, and Google Cloud Storage for storing model artifacts.

Step-by-Step Explanation

Step 1: Set Up Google Cloud Deploy

1. Create a Google Cloud Deploy delivery pipeline.
2. Define the targets (staging, production) in the pipeline.
3. Configure the delivery pipeline to use a GKE cluster.

Step 2: Configure GKE Cluster

1. Create a GKE cluster to host the Machine Learning APIs.
2. Set up namespaces for different environments (staging, production).
3. Deploy the initial version of the APIs to the GKE cluster.

Step 3: Set Up Google Cloud Storage

1. Create a Google Cloud Storage bucket to store model artifacts.
2. Configure the bucket to allow access from the GKE cluster.

Step 4: Define Deployment Strategies

1. Implement canary deployments for progressive delivery.
2. Configure Google Cloud Deploy to use the canary strategy for deploying new versions of the APIs.

Step 5: Monitor and Rollback

1. Set up monitoring for the APIs using Google Cloud Monitoring.
2. Configure automatic rollback in case of deployment failures.

Summary and Conclusion

In this guide, we set up a progressive delivery pipeline for Machine Learning APIs using Google Cloud Deploy. We configured a GKE cluster to host the APIs, set up Google Cloud Storage for model artifacts, and defined deployment strategies for progressive delivery. By following these steps, you can ensure a smooth and controlled rollout of new API versions, with the ability to monitor and rollback if necessary.

Full Code Example

import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";

// Step 1: Set Up Google Cloud Deploy
const deliveryPipeline = new gcp.clouddeploy.DeliveryPipeline("delivery-pipeline", {
    name: "ml-api-delivery-pipeline",
    description: "Delivery pipeline for ML APIs",
    serialPipeline: {
        stages: [
            {
                targetId: "staging",
            },
            {
                targetId: "production",
            },
        ],
    },
    location: "us-central1",
});

const stagingTarget = new gcp.clouddeploy.Target("staging-target", {
    name: "staging",
    description: "Staging environment",
    gke: {
        cluster: "projects/my-project/locations/us-central1/clusters/my-cluster",
    },
    location: "us-central1",
});

const productionTarget = new gcp.clouddeploy.Target("production-target", {
    name: "production",
    description: "Production environment",
    gke: {
        cluster: "projects/my-project/locations/us-central1/clusters/my-cluster",
    },
    location: "us-central1",
});

// Step 2: Configure GKE Cluster
const gkeCluster = new gcp.container.Cluster("ml-api-cluster", {
    name: "ml-api-cluster",
    initialNodeCount: 3,
    location: "us-central1",
    nodeConfig: {
        machineType: "e2-medium",
    },
});

// Step 3: Set Up Google Cloud Storage
const storageBucket = new gcp.storage.Bucket("ml-model-artifacts", {
    name: "ml-model-artifacts",
    location: "US",
});

// Outputs
export const deliveryPipelineUrl = deliveryPipeline.id;
export const gkeClusterEndpoint = gkeCluster.endpoint;
export const storageBucketUrl = storageBucket.url;