1. Version-controlled ML Model Serving on Kubernetes


    To serve a machine learning (ML) model on Kubernetes with version control, you'll want to package your ML model inside a Docker container, deploy it on a Kubernetes cluster, and manage different versions using Kubernetes' native resources along with the help of a registry that stores versions of your models.

    Here is an overview of the process:

    1. Create a Docker Image: Package your trained ML model into a Docker image. This image includes the model files and a web server (such as Flask, FastAPI, etc.) that defines endpoints to interact with the model.

    2. Push to a Registry: Push the Docker image to a container registry such as Docker Hub, AWS ECR, Google GCR, or Azure Container Registry.

    3. Kubernetes Deployment: Define a Kubernetes Deployment resource that specifies your Docker image and the desired number of replicas. Each Deployment is a version of your application.

    4. Kubernetes Service: Expose your Deployment as a Service which provides a stable endpoint for model inference.

    5. Version Control: Manage versions of your Deployment by applying updates to the Docker image tag and rolling out updates via Kubernetes. Use annotations or labels to tag deployments with version information, and automate this process with CI/CD pipelines.

    6. (Optional) Kubernetes Ingress: Define an Ingress resource to manage external access to the ML model services, including routing and SSL termination.

    Below is a Pulumi program in Python that outlines how to create these resources on Kubernetes:

    import pulumi import pulumi_kubernetes as k8s # Configuration for the Docker image of the ML model # Replace 'your-docker-image' and 'your-image-tag' with your Docker image details image_name = 'your-docker-image:latest' model_service_name = 'model-service' # Define the Kubernetes Deployment for the ML model model_deployment = k8s.apps.v1.Deployment( "model-deployment", spec={ "selector": {"matchLabels": {"app": model_service_name}}, "replicas": 1, "template": { "metadata": {"labels": {"app": model_service_name}}, "spec": { "containers": [{ "name": model_service_name, "image": image_name, "ports": [{"containerPort": 8080}], }], }, }, }) # Define the Kubernetes Service for stable networking model_service = k8s.core.v1.Service( "model-service", spec={ "selector": {"app": model_service_name}, "ports": [{"port": 80, "targetPort": 8080}], "type": "LoadBalancer", }) # Export the model service endpoint for easy access pulumi.export('model_service_endpoint', model_service.status.apply(lambda status: status['load_balancer']['ingress'][0]['ip']))


    • We import the necessary Pulumi libraries for Kubernetes interaction.
    • We define the name and tag of your Docker image hosting the ML model.
    • We create a Kubernetes Deployment named model-deployment using a single replica (which can be scaled as needed).
      • The Deployment specifies the Docker image and the container port (8080 in this case) where the model serving API is running.
    • We create a Kubernetes Service named model-service, which will be of type LoadBalancer for this example, to expose our ML model outside of the Kubernetes cluster on port 80 mapping to the target container port 8080.
    • Finally, we export the service endpoint, so that you can easily access the ML model's API once it's running.

    Make sure to replace 'your-docker-image:latest' with your actual Docker image repository and tag, and ensure the ports are correct for your application.

    For version control, whenever you have a new version of the model you want to deploy:

    • Build and push a new Docker image with a different tag to your container registry.
    • Update the image_name variable in the Pulumi program with the new Docker image tag.
    • Re-run pulumi up to perform a rolling update of your service.

    Kubernetes will manage the release of the new version without downtime. By integrating this process into a CI/CD system, every time you push a new image tag, the Pulumi program can be automatically updated and applied, effectively giving you version control of your model serving.