1. Continuous Deployment of AI Services with Harness


    Continuous Deployment (CD) is a software engineering approach where software is automatically built, tested, and deployed to production. When integrating an AI service, the CD process allows for new models, updates, or service features to be deployed rapidly and reliably.

    Harness is a Continuous Delivery platform that simplifies the process of deploying applications to production. With Harness, you can automate the deployment workflows, set up pipelines, monitor services, and more. We'll use Pulumi's Harness provider to define and configure our CD pipeline for AI services deployment.

    Below is a high-level description of the resources and steps we would use to set up a Continuous Deployment pipeline for AI services with Harness using Pulumi:

    1. Organization and Project Setup: We will create an Organization and a Project within Harness to logically group our pipeline and related configurations.
    2. Service Configuration: A service represents our AI application in Harness. This encapsulates everything needed to deploy and verify a version of our application.
    3. Environment Configuration: An environment within Harness represents a deployment target. We would set up an environment that points to where our AI service will run.
    4. Pipeline Configuration: Our CD pipeline that orchestrates the deployment process. We will define steps for our AI service, such as pulling the latest code, running tests, and deploying to the environment.
    5. Policy Definition: If required, we can define policies to enforce certain rules or conditions during the deployment process.

    Here's a Pulumi Python program for setting up a CD pipeline for AI services with Harness. This will cover creating a project, service, environment, and an example pipeline.

    import pulumi import pulumi_harness as harness # Create a Harness Organization (if not already existent) org = harness.Organization("ai-org", name="AI Services Org", identifier="ai-services-org", description="Organization to hold AI services" ) # Create a new Harness Project within the organization project = harness.Project("ai-project", name="AI Services Project", org_id=org.id, color="blue", identifier="ai-services-project", description="Project for AI service deployments", tags=["ai", "cd"] ) # Define a Harness Service for the AI application service = harness.Service("ai-service", name="Predictive Service", org_id=org.id, project_id=project.id, description="A predictive model service", yaml=""" # The YAML spec to configure the Harness service mostly based on docker/k8s/Helm specifications """, tags=["predictive", "ml"] ) # Define a Harness Environment where the AI service will be deployed environment = harness.Environment("prod", name="Production", type="PROD", project_id=project.id, description="Production environment for the AI services", tags=["production"] ) # Define a Harness Pipeline that describes the deployment process pipeline = harness.Pipeline("ai-deployment-pipeline", name="AI Service Deployment Pipeline", project_id=project.id, org_id=org.id, yaml=""" # A YAML pipeline definition that includes steps like building Docker images, deploying to Kubernetes, etc. """, description="Pipeline to handle deployment of AI services", tags=["deployment", "pipeline"] ) # Optional: Define a Harness Policy for additional controls policy = harness.Policy("security-policy", org_id=org.id, project_id=project.id, name="Security Policy", identifier="security-policy", description="Policy for secure deployment", rego=""" # Rego policy specifications to ensure certain conditions are true before deploying """ ) # Export relevant values to be used outside of Pulumi pulumi.export("project_id", project.id) pulumi.export("service_id", service.id) pulumi.export("environment_id", environment.id) pulumi.export("pipeline_id", pipeline.id)

    This program sets the foundation for a continuous deployment pipeline using Harness. The YAML specifications for both the service (service.yaml) and the pipeline (pipeline.yaml) would contain the necessary details about your AI service deployment, such as docker images to use, Kubernetes configurations, test steps, and so on. These specifications are specific to your architectural needs and should be filled in appropriately.

    The comments within the code provide guidance on what each resource represents and placeholders where custom configurations should be provided. Once deployed, the resources will be created within your Harness account, establishing the infrastructure for your CD pipeline. With this in place, you can begin continuously deploying your AI services with confidence.